Author: network

Fairness and bias correction in machine learning for depression prediction across four study populations

A study found that standard machine learning approaches often exhibit biased behaviours in predicting depression across different populations. It also demonstrated that both standard and novel post-hoc-bias mitigation techniques can effectively reduce unfair bias, though no single model achieves equality of outcomes.

The biases that were identified risk reinforcing structural inequalities in mental healthcare, particularly affecting underserved populations. This underscores the importance of analyzing fairness during model selection and transparently reporting the impact of debiasing interventions to ensure equitable healthcare applications.

Learn more about this study here: https://doi.org/10.1038/s41598-024-58427-7

Reference

Dang, V.N., Cascarano, A., Mulder, R.H. et al. Fairness and bias correction in machine learning for depression prediction across four study populations. Sci Rep 14, 7848 (2024).
New-Onset Illness Anxiety Disorder After Helicobacter Pylori Infection: A Case Report

In this interesting case study from 2024, a 21-year-old woman developed illness anxiety disorder (IAD), formerly known as hypochondria, following a Helicobacter pylori infection.

In the case, even after the H. pylori infection was successfully treated, the patient’s anxiety persisted, affecting her daily life and leading to frequent medical visits and avoidance behaviors.

While the study also explored potential psychosocial risk factors that may contribute to developing IAD and discussed both pharmacological and psychological treatment options, this case underscores how physical health events, like infections, can sometimes trigger or exacerbate mental health conditions, emphasizing the importance of integrated care for both gut and mental health.

Learn more about this study here: https://doi.org/10.7759/cureus.52613

Reference

Labban S A, Murshid L, Yousef Alhazmi A, et al. (January 20, 2024) New-Onset Illness Anxiety Disorder After Helicobacter Pylori Infection: A Case Report. Cureus 16(1): e52613.
Value of serum brain-derived neurotrophic factor and glial fibrillary acidic protein for detecting depression in patients with Helicobacter pylori infection

Depression is often associated with Helicobacter pylori and the success of its treatment.

In this recent research paper, it was shown that people infected with these bacteria have lower levels of BDNF (a brain health marker considered a promising biomarker of depression) and higher levels of GFAP (a protein known as a marker of astroglia pathology in depression) in their blood, a change that is strongly related to depression symptoms. The researchers also found that a combined marker including BDNF, GFAP, gut hormone levels, and gastrointestinal symptoms scores was able to accurately predict depression in infected patients.

These findings suggest that these markers could be used to detect depression in people with H. pylori infection.

Learn more about this study here: https://doi.org/10.1016/j.neulet.2024.137687

Reference

Zhao, E., Yu, Q., Wang, M., Wang, Z., Jiang, B., Ma, X., Zhou, B., Dai, Q., Li, J., Wang, S., Chen, F., & Yang, X. (2024). Value of serum brain-derived neurotrophic factor and glial fibrillary acidic protein for detecting depression in patients with Helicobacter pylori infection. Neuroscience letters, 825, 137687.
Helicobacter Pylori and Psychiatric Disorders: Comorbidity and Therapeutic Perspectives

This 2023 observational study, conducted over three consecutive years in a psychiatric clinic, followed adults receiving outpatient psychiatric care for different psychiatric disorders.

Of those patients, 291 had depression that did not respond to treatment, persistent iron deficiency, or digestive complaints, and were tested for the presence of Helicobacter pylori. After confirming the infection and treating it, most patients also received iron and vitamins B9 and B12 when needed.

The results were striking: 74% of H. pylori–positive patients who continued psychiatric follow-up reported improvement after treatment. They experienced reductions in symptoms such as apathy, loss of pleasure, anxiety, sadness, cognitive problems, derealization, and sleep disturbances.

Although these benefits may be linked to several biological mechanisms, including reduced brain inflammation and changes in how tryptophan is metabolized, as well as the removal of toxins produced by the bacteria the researchers suggest that screening and treating H. pylori infection may meaningfully support psychiatric care in selected patients.

Learn more about this study here: https://www.scientificliterature.org/Anxiety/Anxiety-23-136.pdf

Reference

Kassir Adel and Kassir Sarah. Helicobacter Pylori and Psychiatric Disorders: Comorbidity and Therapeutic Perspectives. Anxiety And Depression Journal. 2023; 4(1):136
Turning Evidence Into Action: The AEQUITAS Database

AI tools used in healthcare can carry hidden biases, sometimes performing less accurately for women, ethnic minorities, or other underrepresented groups. But knowing this risk exists in general is one thing. Being able to check whether a specific tool, in a specific clinical area, has documented bias issues is another, and that’s exactly the gap the AEQUITAS Database was built to fill.

The AEQUITAS Database is a structured, digital repository that systematically collects, organises, and provides access to evidence on gender and racial bias in biomedical AI. Rather than leaving healthcare professionals, researchers, and policymakers to piece together scattered studies and reports, it brings this dispersed evidence together into one coherent, accessible knowledge base.

The database is publicly accessible through the AEQUITAS project website, and it was built with a user-oriented approach, meaning both technical and non-technical users can navigate it effectively.

Built for multiple audiences

The Database was designed to serve several groups at once, each with different needs. For healthcare professionals, it’s a resource for identifying potential biases in AI-based diagnostic and decision-support tools, improving patient-centred care, and reducing the risk of misdiagnosis. For civil society organisations, it supports awareness-raising and advocacy for fair, inclusive healthcare systems. And for researchers and policymakers, it feeds into further scientific investigation, the development of clinical guidelines, and regulatory oversight at European level.

A practical tool

The Database is designed to be used, not just consulted occasionally. Before adopting a new AI tool, healthcare professionals can search for entries related to its disease area to check whether bias has been documented in similar tools. When an AI system produces unexpected outputs, the Database can help determine whether it’s a known pattern rather than an isolated anomaly. And when caring for patients from underrepresented groups, it can help clinicians understand whether the tools involved in their care carry documented disparities relevant to that patient’s profile.

The Database is also a living resource that will grow as new evidence becomes available. The more healthcare professionals, researchers, and organisations engage with it, the more valuable it becomes for everyone.
Disaster Resilience, Health Emergency & Crisis Management
Proposal: Fostering Health and Community Resilience

Proposed Implementation: 2024 – 2026

Call: HORIZON-CL3-2023-DRS-01- Disaster-Resilient Society 2023

Proposed Budget: 3 881 825,00€

Keywords: Disaster resilience and crisis management, Health, emergency, communication

Objective: The project aims to enhance societal resilience and support vulnerable groups, particularly during crises. The goals include developing a user-friendly application for efficient communication with vulnerable populations, organizing community resilience training, providing personalized education for disabled groups, fostering local support networks, promoting app awareness, celebrating community strengths, and implementing a rewards system.

These objectives align with the work program theme of increasing society’s resilience and addressing vulnerable groups’ needs during crises. Measurable and verifiable, these goals focus on inclusivity, community building, and effective communication.

Partners:
- Istanbul Universitesi – Cerrahpasa
- Health Citizens – European Institute
- Tech2market Spolka Z Ograniczona Odpowiedzial
- Nuromedia Gmbh
- UC Limburg
- Institoyto Koinonikis Kainotomias Kai Synoxis
- Aristotelio Panepistimio Thessalonikis
- Nyugat-Pannon Terulet-Es Gazdasagfejlesztesi Sz
- Razvojna Agencija Sotla
From Principles to Practice: How AEQUITAS Turns Policy into Action

AI systems used in healthcare are governed by strict ethical principles and legal requirements, from the EU AI Act to the EU Charter of Fundamental Rights. But knowing the rules isn’t the same as knowing how to apply them. Hospitals and civil society organisations (CSOs) need concrete, operational processes they can actually put into practice, day to day, to make sure these tools are used safely and fairly.

This is exactly what the AEQUITAS AI Regulatory Model was designed to do. It gives hospitals and CSOs a practical governance framework covering the full lifecycle of AI systems, from procurement and validation to deployment, monitoring, and accountability.

The model draws inspiration from approaches that have proven effective in other high-risk sectors. It echoes the logic of pharmacovigilance, the systematic tracking of adverse drug reactions, in what AEQUITAS calls an “AI-vigilance” system: a way to report and analyse incidents involving AI-supported clinical decisions, with a particular focus on equity outcomes. It also mirrors the patient safety culture familiar from aviation-inspired approaches in hospitals, built around identifying hazards, reporting incidents, and continuously improving.

At its core, the model is grounded in the EU Charter of Fundamental Rights (2009), particularly the principles of human dignity, equality and non-discrimination, and the right to healthcare, translating these rights into operational safeguards that follow an AI system throughout its use.

The five checkpoints

Rather than treating AI governance as a single box to tick, the model structures oversight around five sequential stages, each one a checkpoint designed to catch potential equity risks before they reach patients. Throughout this process, hospitals act as the main deployers, leading decisions and operational control, while CSOs play an independent oversight role, reviewing documentation, flagging equity-related risks, and conducting audits, without controlling day-to-day clinical decisions themselves.

These stages follow the natural lifecycle of any AI system in a hospital. It starts with scope and classification, determining an AI tool’s regulatory status before it’s even procured or developed. This sets the direction for everything that follows. Next comes procurement, often the moment hospitals have the greatest leverage, as it’s where suppliers can be required to commit to addressing bias risks throughout a system’s lifecycle. At this stage, CSOs are able to review procurement documentation and flag equity concerns. The third stage, validation, means independently evaluating how a system performs, not just overall, but across different patient groups, before it’s ever used with real patients.

Once a system passes validation, the deployment stage ensures the right safeguards are in place: trained oversight staff, mechanisms for clinicians to override AI recommendations, and clear communication with patients about when these tools are being used in their care. And because AI systems aren’t static, the final stage, ongoing monitoring, auditing, and accountability, keeps watching for emerging disparities long after a system goes live, with CSOs conducting independent audits and helping escalate persistent issues.

Building a shared network across Europe

Beyond the model itself, AEQUITAS is currently establishing a European network of CSOs and public hospitals. This shared governance layer means that insights, incidents, and mitigation strategies identified in one country contribute to a growing, Europe-wide knowledge base, one that benefits patients well beyond the institution where the issue was first spotted.

Together, these tools turn abstract principles into something hospitals and CSOs can actually act on, closing the gap between good intentions and equitable, everyday care.

References:

European Union. (2009). Charter of Fundamental Rights of the European Union. Official Journal of the European Union, C 303/1. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:12012P/TXT
Neuromodulating agents in functional dyspepsia: a comprehensive review

Functional dyspepsia is a frequent chronic condition characterized by upper abdominal discomfort without an identifiable organic cause. Usual first-line treatments include proton pump inhibitors or Helicobacter pylori eradication, but many patients continue to have persistent symptoms. Because of this, neuromodulating agents are frequently used in clinical practice, although current European, American and Canadian guidelines primarily mention tricyclic antidepressants (TCAs).

This review analyzed randomized controlled trials in adults meeting Rome criteria or with normal endoscopy findings. Out of 386 studies screened, 14 met inclusion criteria.

The findings indicate that TCAs, such as amitriptyline and imipramine, show the strongest evidence of benefit for symptom relief in functional dyspepsia. Other agents, including tetracyclic antidepressants, levosulpiride, and anxiolytics, may be helpful, but current data are insufficient to draw firm conclusions. By contrast, SSRIs and SNRIs do not appear effective for this condition.

Learn more about this review here: https://doi.org/10.51821/86.1.10998

Reference

Bosman, L., Wauters, L., & Vanuytsel, T. (2023). Neuromodulating agents in functional dyspepsia: a comprehensive review. Acta gastro-enterologica Belgica, 86(1), 49–57
Improving Health in Alzheimer’s Patients
Proposal: Adapted Physical Activity and Awareness Training to Improve Health in Alzheimer’s Patients and Their Entourage

Proposed Implementation: 2024 to 2026

Call: EU4H-2023-PJ – EU4H Action Grants 2023

Proposed Budget: 596 289,60€

Keywords: Alzheimer, Physical Activity Practices, Physical Activity, Health, Inequality

Objective: Dementia is one of the most significant public health concerns in the European Union with around 10 million people living with this condition. The prevalence of Alzheimer’s disease is expected to double in the next 30 years with the increase in the aging population. As a consequence, a part of the population will risk becoming dependent on carers and specific socio-medical services.

As a result, this will increase health costs for the EU. The project’s overall objective is to contribute to prevent dependency, thus reducing the expenditure of the European healthcare system on Alzheimer disease and other neurological cognitive impairments by providing to patients who have been diagnosed with Alzheimer and/or dementia with adapted physical activity practices to help slow down the progress of the disease and manage their symptoms.

To achieve these objectives, actions will focus on people who have been diagnosed with dementia at a pre-symptomatic early stage and with MCI-minor cognitive impairment. First, this will be achieved through a person-centered physical activity programme with the support of functional ability training and guidelines for a healthier lifestyle. It will support 500 patients by means of 50 person-centered physical activity sessions over the course of one year across 3 European countries.

Second, the project aims to raise awareness on physical activity as a tool to manage neurological diseases and as a tool to prevent the progression of cognitive impairment symptoms (such as memory loss, problems communicating etc.) and to create an integrated care model for dementia patients with a 3-staged approach 1st test, 2nd evaluate and 3rd implement. The project will provide a sustainable model by constant evaluation process and sharing the results across the European member states.

Third, an online “Train the Trainer” programme will provide a foundation for further upskilling of health care practitioners, physical activity trainers and caregivers.

Partners:
- Fundacion Siel Bleu España
- Health Citizens – European Institute
- Siel Bleu Ireland
- Sport Initiative et Loisir Bleu Association
- Alzheimer Catalunya Fundació
Association of mental health conditions and functional gastrointestinal disorders among Vietnamese new-entry medical students

Functional gastrointestinal disorders (FGIDs), sometimes called disorders of gut–brain interaction, do not affect only older people, they are also common in young adults. A study among 400 first-year medical students in Vietnam investigated how these gut disorders relate to mental health.

About 10% of students had an FGID such as functional dyspepsia or irritable bowel syndrome, and 3% had overlapping conditions including acid reflux. Depression and anxiety were also frequent, with around 10% showing signs of major depressive disorder and 7% showing generalized anxiety disorder. Helicobacter pylori infection was detected in 45% of participants.

The key finding was that depression was strongly associated with gut disorders. Students with major depressive disorder were much more likely to have FGIDs and overlapping gastrointestinal problems than students without depression.

Interestingly, in this study, this risk was also greater in women.

These results support the strong link between mental health and digestive symptoms in young adults, highlighting the importance of screening and early support for both areas in student populations.

Learn more about this study here: https://doi.org/10.1371/journal.pone.0289123

Reference

Tran, T. T. T., Luu, M. N., Tran, L. L., Nguyen, D., Quach, D. T., & Hiyama, T. (2023). Association of mental health conditions and functional gastrointestinal disorders among Vietnamese new-entry medical students. PloS one, 18(7), e0289123.
Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression

The systematic review and meta-analysis found that wearable AI systems demonstrate promising performance in detecting and predicting depression. However, substantial variability exists among algorithms and devices, thereby indicating that performance can vary significantly.

What this means is that disparities across different algorithms and devices were identified, suggesting that certain demographic groups may be underrepresented or inadequately served by current wearable AI systems. This variability underscores the need for further research to enhance the generalizability and fairness of these technologies in clinical practice.

Learn more about this review here: https://doi.org/10.1038/s41746 -023-00828-5

Reference

Abd-Alrazaq, A., AlSaad, R., Shuweihdi, F. et al. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. npj Digit. Med. 6, 84 (2023).
Helicobacter Pylori Associated Depression among Patients Presenting with Epigastric Pain

Helicobacter pylori infection is an extremely prevalent infection that has been connected not only to a number of illnesses such as stomach cancer and peptic ulcer disease. H. pylori has also been linked to depression but the mechanisms behind this connection are still poorly understood.

A research study by Mohamed and colleagues aimed to better explore this relation by assessing the presence and severity of depression in 150 patients with or without H. pylori infection.

When comparing both groups, the authors found that H. pylori infection was significantly associated with depression and, more interestingly, with its severity. These findings point to the importance of assessing the presence of gastrointestinal symptoms or dyspepsia in patients with depression.

Learn more about this study here: https://healthcitizens.org/wp-content/uploads/Mohamed-and-Elrassas_Helicobacter-Pylori-.pdf

Reference

Dina M. Mohamed and Hanan Elrassas, “Helicobacter Pylori Associated Depression among Patients Presenting with Epigastric Pain”, The Egyptian Journal of Hospital Medicine (January 2023) Vol. 90 (2), Page 2315-2320
Beyond Ethics: How Europe Is Regulating AI in Healthcare

Knowing that AI can be biased is one thing. Knowing what to do about it, legally and ethically, is another.

Medicine’s ethical foundations, extended to AI

Medicine has long been associated with high ethical standards, from the Hippocratic Oath to the Declarations of Geneva and Helsinki. Unfortunately, the history of medicine includes deeply troubling chapters, from eugenics, forced sterilisation of indigenous and marginalised populations, to non-consensual experimentation on people with disabilities. These historical failures are directly relevant today because the structural injustices behind them shaped decades of medical data collection, and their legacy is exactly what today’s AI systems risk inheriting when trained on that same data.

Biomedical ethics is built around four core ethical principles: autonomy (respecting a person’s right to make their own decisions), non-maleficence (avoiding harm), beneficence (actively promoting wellbeing), and justice (distributing benefits and risks fairly, especially given known disparities based on race, gender, and social status) (Beauchamp & Childress, 2019).

As AI entered clinical practice, ethicists added a fifth principle specifically for these technologies: explicability, the idea that the reasoning behind an AI system’s decisions must be understandable and open to scrutiny, not simply accepted at face value (Floridi et al., 2018).

The EU’s legal response

The European Union has built a multi-layered legal framework specifically to address these risks. At its centre is the AI Act, which classifies many healthcare applications, including diagnostic tools and clinical decision-support systems, as “high-risk.” This means they’re subject to strict requirements: providers must actively identify and mitigate risks of discriminatory outcomes, ensure meaningful human oversight, and maintain accuracy and transparency throughout the system’s lifecycle (EU AI Act, 2024).

Hospitals that deploy these systems have specific legal obligations too, including using AI in line with provider instructions, assigning competent human oversight, maintaining logs of system operation, and reporting serious incidents, particularly where there’s a risk of unequal outcomes across patient groups.

The AI Act doesn’t stand alone. It works alongside the Medical Device Regulation (MDR; European Union, 2017a) and In Vitro Diagnostic Regulation (IVDR; European Union, 2017b), which govern the safety of medical technologies, the GDPR (European Union, 2016), which protects the sensitive health data these systems rely on, and the emerging European Health Data Space (EHDS; European Union, 2025), designed to enable more representative datasets for future AI development. Together, they form a legal ecosystem, all ultimately grounded in the EU Charter of Fundamental Rights (2009), particularly its guarantees of non-discrimination, equality, and the right to healthcare.

All these policies translate into real responsibilities for hospitals and healthcare professionals, from understanding a tool’s limitations and knowing when to override its recommendations, to being transparent with patients about when and how these systems are used in their care.

Beyond raising awareness, the AEQUITAS project has developed a practical AI Regulatory Model that translates these legal and ethical principles into concrete steps hospitals and healthcare professionals can actually apply, turning complex EU legislation into something usable in everyday clinical practice.

References:

Beauchamp, T. L., & Childress, J. F. (2019). Principles of biomedical ethics (8th ed).

European Commission, Timeline for Implementation of the EU AI Act, Brussels: European Commission, 2024.

European Union. (2009). Charter of Fundamental Rights of the European Union. Official Journal of the European Union, C 303/1. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:12012P/TXT

European Union, Regulation (EU) 2016/679 on General Data Protection Regulation (GDPR), Brussels: EU, 2016.

European Union, Regulation (EU) 2017/745 on Medical Devices (MDR), Official Journal of the European Union, L117, Brussels: European Union, 2017.

European Union, Regulation (EU) 2017/746 on In Vitro Diagnostic Medical Devices (IVDR), Official Journal of the European Union, L117, Brussels: European Union, 2017.

European Union, Regulation (EU) 2025/327 on the European Health Data Space (EHDS), Brussels: European Union, 2025.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
When “Neutral” Data Isn’t: Gender and Racial Bias in Biomedical AI

Bias in AI isn’t abstract. It shows up in real diagnoses, real treatment decisions, and real patients, particularly those who already face multiple layers of disadvantage. Discrimination in AI is an intersectional issue, overlapping with age, gender, race, sexual orientation, and health status, among other dimensions of identity. The patients most at risk of being harmed by biased AI tools are often those who already face the most barriers to quality care.

Decades of biased medical data

The biases we see in AI didn’t come from nowhere. They’re a direct consequence of medical data collected over decades, data shaped by systemic discrimination and structural inequalities in healthcare research and practice (Cirillo et al., 2020; Cross et al., 2024).

For much of medical history, clinical and experimental studies focused predominantly on male participants. This is what is known as the gender health gap. In 2020, only 5% of global health research funding went to women’s health research. This was split into 4% for women’s cancers and 1% for all other women-specific health conditions, with 25% of that further limited to fertility research (Nature Reviews Bioengineering, 2024).

It’s worth noting this isn’t a one-way problem: in some areas, such as depression, men are underrepresented in clinical data, largely because they’re less likely to seek care, report symptoms, or receive a diagnosis (Smith et al., 2018).

Racial bias is equally well documented

Racial bias in medicine is also extensively documented, particularly in the United States where research shows that, for instance, Black patients and other minority groups receive fewer medical procedures, lower rates of surgical intervention, and fewer referrals to specialists than white patients, regardless of clinical need (Bowser, 2001; Williams & Wyatt, 2015). When AI systems are trained on data that reflects decades of existing inequalities in healthcare access and treatment, they risk encoding and perpetuating those inequalities at a much larger scale. While much of the evidence comes from the US, the structural conditions producing racial bias, including socioeconomic inequalities, barriers to access and underrepresentation in research, are present across Europe too.

LGBTQIA+ patients face their own layer of risk

It is also important to recognise the specific situation of LGBTQIA+ individuals, who experience discrimination in healthcare and are subject to stereotypes that affect the care they receive. These social and cultural factors perpetuate discrimination and have a measurable impact on health and healthcare. Research has shown that 16% of LGBTQIA+ individuals report discrimination in healthcare encounters, and 18% avoid seeking care altogether due to fear of mistreatment (Chang et al., 2025). Another large-scale study found that in emergency department scenarios, AI recommendations for LGBTQIA+ patients included mental health interventions six to seven times more often than was clinically appropriate (Chang et al., 2025).

How this plays out in three key areas

In cardiovascular care, women’s symptoms often differ from the “classic” presentation described in medical textbooks, itself largely based on male patient data (Fatunde et al., 2025). As a result, women are offered fewer diagnostic tests, less medication, and fewer specialist referrals (Al Hamid et al., 2024). Racial bias compounds the problem: pulse oximeters, devices routinely used to measure blood oxygen saturation, have been shown to produce less accurate readings for patients with darker skin tones (Sjoding et al., 2020), a bias that then feeds directly into AI-based triage systems.

In diabetes care, racial bias is extensively documented. Studies have shown that African American patients present systematically higher A1c values than white patients with the same average blood glucose (Karter et al., 2023). If an AI system uses A1c as a proxy for glycaemic control without accounting for this, it risks producing incorrect diagnoses for Black patients.

In depression, both gender and racial bias carry significant weight, especially in tools using natural language processing for screening. Men and women tend to express psychological distress differently (Pennebaker et al., 2003), so a system trained predominantly on one gender’s language patterns may screen inaccurately for the other. Most mental health AI tools also still operate on binary gender assumptions, excluding non-binary, transgender, and gender non-conforming individuals from both the data and the populations these tools are meant to serve (Hafner et al., 2024).

Why this matters

These biases translate into delayed diagnoses, inappropriate treatments, and unequal care, every day, for real patients. Understanding these patterns is exactly what equips healthcare professionals to ask better questions, challenge assumptions, and advocate for the patients most at risk, which is precisely what the AEQUITAS training is designed to do.

References:

Chang, C.T., Srivathsa N., Bou-Khalil, C., Swaminathan, A., Lunn, M.R., Mishra, K., Koyejo, S,. Daneshjou, R. (2025). Evaluating anti-LGBTQIA+ medical bias in large language models. PLOS Digit Health 4(9): e0001001. https://doi.org/10.1371/journal.pdig.0001001

Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A.A., Valencia, A., Rementeria, M.J., Chadha, A.S., & Mavridis, N. (2020). Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. npj Digit. Med. 3(81). https://doi.org/10.1038/s41746-020-0288-5

Cross, J. L., Choma, M. A., & Onofrey, J. A. (2024). Bias in medical AI: Implications for clinical decision-making. PLOS Digital Health, 3(11), e0000651. https://doi.org/10.1371/journal.pdig.0000651

Funding research on women’s health. (2024). Nature Reviews Bioengineering, 2, 797–798. https://doi.org/10.1038/s44222-024-00253-7

Hafner, F.S., Valdivia, A., Rocher, L. 2025. Gender Trouble in Language Models: An Empirical Audit Guided by Gender Performativity Theory. FAccT ’25: Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 1677–1695. https://doi.org/10.1145/3715275.3732112

Karter, A. J., Parker, M. M., Moffet, H. H., & Gilliam, L. K. (2023). Racial and Ethnic Differences in the Association Between Mean Glucose and Hemoglobin A1c. Diabetes Technology & Therapeutics, 25(10), 697–704. https://doi.org/10.1089/dia.2023.0153

Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological Aspects of Natural Language Use: Our Words, Our Selves. Annual Review of Psychology, 54(1), 547–577. https://doi.org/10.1146/annurev.psych.54.101601.145041

Sjoding, M. W., Dickson, R. P., Iwashyna, T. J., Gay, S. E., & Valley, T. S. (2020). Racial Bias in Pulse Oximetry Measurement. New England Journal of Medicine, 383(25), 2477–2478. https://doi.org/10.1056/NEJMc2029240

Smith, D.T., Mouzon, D.M., & Elliott, M. (2018). Reviewing the assumptions about men’s mental health: An exploration of the gender binary. American Journal of Men’s Health, 12(1), 78–89. https://doi.org/10.1177/1557988316630953

Bowser, R. (2001). Racial bias in medical treatment. Dick. L. Rev., 105(3), 365.

Williams, D. R., & Wyatt, R. (2015). Racial Bias in Health Care and Health: Challenges and Opportunities. JAMA, 314(6), 555. https://doi.org/10.1001/jama.2015.9260
The Hidden Ways AI Can Discriminate

When we talk about bias in AI, we are referring to “computer systems that systematically and unfairly discriminate against certain individuals or groups in favour of others” (Friedman & Nissenbaum, 1996). This is not about occasional errors, but about consistent, predictable patterns of unfair outcomes. Furthermore, biases in AI systems are complex, as they can enter the system at almost any stage, from the data it’s trained on to the way it’s ultimately used in a hospital setting. Recognising where bias originates, and how different types can reinforce each other, is the first step towards addressing it.

Three broad roots of bias

Researchers have identified three broad categories of bias that apply to all computer systems. The first is pre-existing bias, which comes from existing inequalities in society that get absorbed into the system, sometimes without anyone realising it (Friedman & Nissenbaum, 1996). The second is technical bias, which arises from the practical compromises made when translating complex human realities into clean computational form. And the third is emergent bias, which appears only after a system is deployed, as the world around it changes in ways the original design never anticipated.

Bias in the AI pipeline

These three categories give us a useful starting point. But for AI and machine learning specifically, researchers have identified seven more precise bias types that can affect these systems throughout their development and use (Suresh & Guttag, 2021):

Historical bias reflects prejudices and stereotypes already present in training data, even if the data is technically accurate. A 1990 study found that after coronary bypass surgery, male patients received pain medication significantly more often than female patients, who were instead given sedatives more frequently (Calderone, 1990). An AI trained on data like this would learn to replicate that same pattern, perpetuating the very inequality it inherited.

Representation bias happens when certain groups are underrepresented in training data. An AI trained mostly on data from one demographic will simply perform worse for everyone else. A well-known example is skin cancer detection tools that are significantly less accurate for patients with darker skin tones because the training datasets contained predominantly images from fair-skinned individuals (Guo et al., 2021).

Measurement bias creeps in when the way something is measured isn’t equally accurate or fair across groups. One striking case involved an algorithm that used healthcare costs as a proxy for how sick someone actually was, and that ended up disadvantaging Black patients, who, facing disproportionate levels of poverty, tended to spend less on healthcare than equally sick white patients (Obermeyer et al., 2019).

Aggregation bias occurs when a single, one-size-fits-all model is applied to genuinely diverse populations, ignoring the fact that the same data point can mean very different things depending on a person’s background.

Learning bias emerges from technical decisions made while building the model itself, choices that can amplify disparities already present in the data, sometimes without the developers being aware. Research has shown, for instance, that differential privacy, a technique meant to protect patient confidentiality, can end up reducing a model’s accuracy for underrepresented groups even further (Bagdasaryan & Shmatikov, 2019).

Evaluation bias happens when the datasets used to test and benchmark the model don’t reflect the real population it will actually serve, meaning a system can look accurate on paper while quietly failing specific groups of patients in practice.

Finally, deployment bias arises when a tool is used in a context very different from the one it was designed and tested for, undermining its reliability in ways that are easy to miss.

Why this matters for patients

Understanding where bias comes from is the first step to catching it, and to making sure the AI tools shaping modern medicine work fairly for everyone, not just the patients who happen to be well represented in the data.

This is exactly the kind of practical, structured understanding the AEQUITAS training equips healthcare professionals with, helping them recognise these patterns before they translate into real harm for real patients.

References:

Bagdasaryan, E., & Shmatikov, V. (2019). Differential Privacy Has Disparate Impact on Model Accuracy (arXiv:1905.12101). arXiv. https://doi.org/10.48550/arXiv.1905.12101

Calderone, K. L. (1990). The influence of gender on the frequency of pain and sedative medication administered to postoperative patients. Sex Roles, 23(11), 713–725. https://doi.org/10.1007/BF00289259

Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems, 14(3), 330–347. https://doi.org/10.1145/230538.230561

Guo, L. N., Lee, M. S., Kassamali, B., Mita, C., & Nambudiri, V. E. (2021). Bias in, bias out: Underreporting and underrepresentation of diverse skin types in machine learning research for skin cancer detection-A scoping review. Journal of the American Academy of Dermatology, S0190-9622(21)02086-7. https://doi.org/10.1016/j.jaad.2021.06.884

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342

Suresh, H., & Guttag, J. (2021). A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO ’21, 1–9. https://doi.org/10.1145/3465416.3483305
Artificial intelligence bias in the prediction and detection of cardiovascular disease

AI can help identify people at risk of cardiovascular disease, detect it early and allow timely intervention. However, bias can arise at any step in the development, validation and use of these algorithms, causing them to perform poorly for historically marginalised groups. This article discusses the sources and consequences of AI bias in predicting and detecting cardiovascular disease, and presents strategies to reduce it.

Bias can come from training data that does not represent the population a tool is meant for, from measurements or devices that work less well for some groups, and from leaving out important factors such as social determinants of health. It can also reflect inequalities in care: because women and ethnic minority patients are more likely to have their cardiovascular disease missed or diagnosed late, algorithms trained on this data may be less accurate at detecting it in these groups. Unequal access to digital technologies can further limit how well some groups are represented in the data. The authors give several examples of AI models that performed worse for women, Black patients or older adults.

To reduce bias, the authors propose an AI health equity framework covering the whole lifecycle of an algorithm: diverse research teams, early involvement of patients, representative and balanced data, the inclusion of social determinants of health, input from clinicians, validation across different groups, transparent reporting of results and monitoring after implementation. They conclude that, without these measures, AI bias could worsen existing healthcare inequities and miss opportunities to predict and detect cardiovascular disease.

Learn more about this article here: https://doi.org/10.1038/s44325-024-00031-9

Reference:

Mihan, A., Pandey, A., & Van Spall, H.G.C. (2024). Artificial intelligence bias in the prediction and detection of cardiovascular disease. npj Cardiovascular Health, 1, 31.
AI Meets Healthcare: Understanding the Promise and the Risks

Artificial intelligence is no longer a distant, futuristic concept in medicine. It’s already here, and is becoming an everyday part of clinical practice, supporting diagnosis, clinical decision-making, treatment planning, and patient risk prediction. In fact, according to the World Health Organization, nearly three quarters of EU countries are now using AI-assisted diagnostics in some form, and that number is only expected to grow (Alowais et al., 2023; World Health Organization, 2026).

As the use of AI promises to further revolutionize the sector, what should we know before trusting it with our health?

How AI works

At its core, biomedical AI relies on three main approaches. Machine learning allows systems to learn patterns from data and make predictions without being explicitly programmed for every scenario. Deep learning, a more advanced form of machine learning, uses layered neural networks (loosely inspired by the human brain) to process complex information like medical images or clinical notes. And natural language processing (NLP) enables computers to understand and interpret human language, such as a doctor’s written notes or a patient’s description of their symptoms.

Real examples already in use

In cardiovascular care, AI tools are helping detect arrhythmias from ECG readings and estimating a patient’s individual risk of heart disease. In diabetes management, AI-powered retinal scans can catch signs of diabetic retinopathy earlier than ever before. And in mental health, natural language processing tools are being explored to help identify signs of depression in the way people write or speak, and to support patients between therapy sessions.

So what’s the catch?

Research has shown that AI applications can lead to misinterpretation and the promotion of biases and discrimination (Varona & Suarez, 2022). In essence, AI systems are only as good as the data they learn from. If that data reflects existing inequalities, for example, if it comes mostly from male patients, or predominantly from one ethnic group, the AI will inherit those biases. It will perform well for the people it “knows,” and poorly for everyone else.

Several documented cases have shown AI tools trained on biased datasets producing less accurate results for underrepresented patients, sometimes with real consequences for diagnosis and treatment.

What AEQUITAS is doing about it

This is exactly the challenge our AEQUITAS project, funded by the European Union, was designed to address. By developing training resources, a database documenting real cases of bias in biomedical AI, and a practical regulatory model for hospitals and CSOs, AEQUITAS is helping healthcare professionals and institutions across Europe use these powerful tools more safely, and more fairly, for every patient.

Understanding how AI works, and where it can go wrong, is the first step toward making sure it works for everyone.

References:

Alowais, S.A., Alghamdi, S.S., Alsuhebany, N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 23, 689 (2023). https://doi.org/10.1186/s12909-023-04698-z

Varona, D., & Suárez, J. L. (2022). Discrimination, Bias, Fairness, and Trustworthy AI. Applied Sciences, 12(12), 5826. https://doi.org/10.3390/app12125826

World Health Organization. 2026. “Artificial intelligence is reshaping health systems: state of readiness across the European Union.” World Health Organization.
The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities – a scoping review

AI health algorithms are increasingly used to predict risk and support treatment decisions, often outperforming humans in diagnostic accuracy. However, they can also underestimate risk for marginalised and underserved patients, delaying the treatment they need. This scoping review analysed 23 sources published between 2020 and 2024 to examine racial and ethnic bias in these algorithms, the role of oversight and stakeholders, and how this bias can be reduced.

A common explanation for this bias is that algorithms are trained on data in which majority populations are overrepresented, making them less reliable for others. Black, Native American, Asian and Latinx populations are often underrepresented in health datasets. Bias can also come from people: racial stereotypes absorbed during medical training can affect how clinicians assess patients, and these assessments end up in the health records used to train algorithms. The authors also describe a feedback loop: when patients from marginalised groups distrust the health system and use it less, data about them becomes even less representative, and the algorithms less accurate for them.

To address this, the review highlights the need for continuous monitoring of algorithms once they are in use, in a similar way to how medicines are monitored after approval, and for retraining or withdrawing those that create inequities. It also stresses that simply including or removing race from an algorithm does not solve the problem, and that responsibility should be shared by developers, health systems, clinicians, regulators and patients. The authors conclude that, without this oversight, AI in healthcare risks entrenching health inequities.

Learn more about this review here: https://doi.org/10.1080/13557858.2024.2422848

Reference:

Hussain, S.A., Bresnahan, M., & Zhuang, J. (2025). The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities – a scoping review. Ethnicity & Health, 30(2), 197–214.
Gender Bias in Diagnosis, Prevention, and Treatment of Cardiovascular Diseases: A Systematic Review

Cardiovascular disease is a leading cause of death for both women and men, but there is growing evidence that women are not always diagnosed and treated in the same way. This systematic review analysed 19 studies to examine gender bias in the diagnosis, prevention and treatment of cardiovascular diseases.

The review found that women’s cardiovascular disease was less often recorded when their symptoms were milder than men’s, or when they were misdiagnosed as digestive or anxiety-related problems. Women were offered fewer diagnostic tests, such as electrocardiograms and coronary angiography, were less often referred to cardiologists or admitted to hospital, and, once in hospital, were less likely to receive coronary interventions. They were also prescribed cardiovascular medicines less often than men, except for medicines for high blood pressure and angina, and saw themselves as being at lower risk of cardiovascular disease.

The authors conclude that women receive fewer diagnostic tests and medicines than men, and link these differences to limited knowledge of how cardiovascular disease presents differently in women and men.

Learn more about this review here: https://doi.org/10.7759/cureus.54264

Reference:

Al Hamid, A., Beckett, R., Wilson, M., Jalal, Z., Cheema, E., Al-Jumeily, D., Coombs, T., Ralebitso-Senior, K., & Assi, S. (2024). Gender Bias in Diagnosis, Prevention, and Treatment of Cardiovascular Diseases: A Systematic Review. Cureus, 16(2), e54264.
Addressing hidden risks: Systematic review of artificial intelligence biases across racial and ethnic groups in cardiovascular diseases

AI models are increasingly used in cardiovascular medicine, but there are concerns that they may not work equally well for people of different racial and ethnic backgrounds. This systematic review examined how accurate and clinically applicable AI models for diagnosing heart disease and predicting outcomes are across racial and ethnic groups.

Of 1,704 references screened, 11 studies were included, covering diagnosis, prognosis and the analysis of heart images. Nine of them (82%) found racial or ethnic bias, while two found no differences between groups. For example, an AI model predicting heart failure from electrocardiograms was less accurate for young Black patients than for young Asian or White patients, and AI tools analysing cardiac MRI scans were more accurate for White people than for people from minority ethnic groups.

The authors conclude that AI models can perform well for some groups and less accurately for others, and that bias should be addressed at every stage, from data collection to clinical use. They recommend methods to reduce bias, standardised ways of measuring fairness, considering social determinants of health rather than race alone, and greater transparency through the sharing of code and data.

Learn more about this review here: https://doi.org/10.1016/j.ejrad.2024.111867

Reference:

Cau, R., Pisu, F., Suri, J.S., & Saba, L. (2024). Addressing hidden risks: Systematic review of artificial intelligence biases across racial and ethnic groups in cardiovascular diseases. European Journal of Radiology, 111867.
Artificial Intelligence to Promote Racial and Ethnic Cardiovascular Health Equity

AI has been seen as a tool that could help detect and reduce the racial and ethnic inequities that persist in cardiovascular care. However, AI can also carry and spread biases of its own, which means it must be approached with care. This review looks at the role of AI in cardiology in identifying and addressing these inequities, and in promoting diversity in research, as well as the concerns its use raises and strategies for using it fairly.

The authors describe how AI has already helped reduce some inequities. In one study, an AI tool identified patients with severe heart valve disease and alerted their doctors, improving follow-up care for patients of all races. Even so, Black patients still faced greater delays in receiving appropriate care than White patients, showing that AI is not a “silver bullet” for eliminating inequities. AI can also perpetuate existing biases, due to limited diversity in training data, biases within the datasets themselves, and inadequate mechanisms for detecting and monitoring bias. For example, an AI model trained mostly on data from White patients to predict heart failure from electrocardiograms performed worse for Black patients, and particularly for Black women.

To use AI in cardiovascular care in a way that addresses racial and ethnic inequities, the authors stress the need for careful design, starting with meticulous data collection and a thorough review of training data. They also call for rigorous testing and continuous monitoring of algorithms to ensure they perform equally well for all groups, as well as greater diversity in the AI workforce and the involvement of stakeholders.

Learn more about this review here: https://doi.org/10.1007/s12170-024-00745-6

Reference:

Amponsah, D., Thamman, R., Brandt, E., James, C., Spector-Bagdady, K., & Yong, C.M. (2024). Artificial Intelligence to Promote Racial and Ethnic Cardiovascular Health Equity. Current Cardiovascular Risk Reports, 18(11), 153–162.
Algorithmic Bias in AI-Based Diabetes Care: Systematic Review of Model Performance, Equity Reporting, and Physiological Label Bias

AI has transformed diabetes management, particularly through tools that predict glucose levels and support clinical decisions. However, there are concerns that these technologies may not work equally well for different racial, ethnic and socioeconomic groups. This systematic review assessed how fair AI-based glucose prediction models are, focusing on differences in performance between groups, how fairness is reported, and biases linked to physiological measures.

Of 1,243 articles initially identified, only 10 met the criteria for inclusion. Just one of them explicitly evaluated whether AI model performance differed by race. Reporting on fairness was rare: only 7% of AI diabetes studies documented ethnic or racial data, and almost none carried out fairness audits. The review also highlights known physiological differences: for the same average glucose level, African American patients tend to have higher HbA1c values than White patients. If HbA1c is used as a reference to train AI models, this difference can be built into them. Yet none of the AI studies reviewed took this into account.

The authors also note that traditional diabetes risk scores overestimate risk for White individuals and underestimate it for Black individuals, a bias that AI systems trained on these scores could inherit. They conclude that, without systematic efforts to reduce bias, AI-based diabetes technologies risk worsening existing health disparities, and call for future research to prioritise transparency, representativeness and fairness.

Learn more about this review here: https://doi.org/10.61186/ist.202502.05.04

Reference:

Alipour, M., & Alipour, A. (2025). Algorithmic Bias in AI-Based Diabetes Care: Systematic Review of Model Performance, Equity Reporting, and Physiological Label Bias. InfoScience Trends, 2(5), 33–46.
Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

Machine learning models can help predict which patients with diabetes are likely to be readmitted to hospital, but they need to work fairly for all patients. This study compared several of these models, including deep learning, generalised linear models, gradient boosting machines and naive Bayes, assessing not only their accuracy but also how fair their predictions were across age, gender and race.

The gradient boosting model performed best overall, with an accuracy of 82.2% and consistently low error rates for most racial and gender groups. However, across several of the models, African American and Hispanic patients had higher rates of false positives, meaning they were more likely to be wrongly predicted to be readmitted. No single model performed best for every group: some worked better for certain groups of patients and others for other groups.

The authors conclude that there is no one-size-fits-all solution, and that models need to be chosen and adjusted carefully to ensure they are both accurate and fair for all patients. According to them, fair algorithms can help reduce disparities and improve outcomes for patients with diabetes from all backgrounds.

Learn more about this study here: https://doi.org/10.1109/ICHI61247.2024.00105

Reference:

Al-Zanbouri, Z., Sharma, G., & Raza, S. (2024). Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions. In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) (pp. 660–669). IEEE.
Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study

Large language models such as GPT-4 are seen as promising tools in health care, from automating administrative tasks to supporting clinical decisions. However, they also risk perpetuating biases and giving incorrect diagnoses, with a direct impact on patient care. This study assessed whether GPT-4 shows racial and gender biases in four possible uses: medical education, diagnostic reasoning, treatment planning and assessments of patients.

The researchers used clinical cases from medical education resources and from published research on implicit bias in health care. When asked to create clinical cases, GPT-4 did not reflect the real demographic diversity of medical conditions, and consistently produced cases that stereotyped how conditions present in different groups. For the same clinical cases, its lists of possible diagnoses were more likely to include diagnoses that stereotype certain races, ethnicities and genders.

The treatment plans and assessments it produced also varied with patients’ demographic characteristics, including recommendations for more expensive procedures and differences in how patients were perceived. The authors conclude that tools like GPT-4 urgently need thorough and transparent bias assessments for each intended use before they are introduced into clinical care.

Learn more about this study here: https://doi.org/10.1016/S2589-7500(23)00225-X

Reference:

Zack, T., Lehman, E., Suzgun, M., Rodriguez, J.A., Celi, L.A., Gichoya, J., Jurafsky, D., Szolovits, P., Bates, D.W., Abdulnour, R.-E.E., Butte, A.J., & Alsentzer, E. (2024). Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study. The Lancet Digital Health, 6(1), e12–e22.
Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes

Continuous glucose monitors, small sensors worn on the body that track blood sugar levels throughout the day, are increasingly combined with AI models that predict where glucose levels are heading. Because White people are overrepresented in medical research, these models may perform worse for other groups if they are trained mainly on White patients’ data. This study tested this using data from 205 adults and children with type 1 diabetes in the US, 101 White and 104 Black.

The researchers trained AI models to predict glucose levels 60 minutes ahead, using datasets with different proportions of White and Black participants, from 0% to 100% White. As the proportion of White participants in the training data increased, prediction errors grew slightly more for Black participants than for White participants. The difference was small but statistically significant.

When the models were then personalised to each individual, using a technique called transfer learning, this difference became smaller and was no longer statistically significant. The authors note that the differences may not be large enough to matter clinically on their own, but conclude that diverse training data is important for fair prediction models, and that personalisation may help build fairer tools.

Learn more about this study here: https://doi.org/10.1371/journal.pdig.0000918

Reference:

Thomsen, H.B., Li, L.Y., Isaksen, A.A., Lebiecka-Johansen, B., Bour, C., Fagherazzi, G., van Doorn, W.P.T.M., Varga, T.V., & Hulman, A. (2025). Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes. PLOS Digital Health, 4(6), e0000918.
Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia

Diabetic retinopathy, damage to the retina caused by diabetes, can lead to vision loss if it is not detected early. This study tested an AI system for diabetic retinopathy screening in real-world conditions, in two endocrinology clinics in Melbourne and three Aboriginal medical services in Western Australia, and also gathered the views of patients and health professionals.

Of the 236 people screened, 203 were included in the main analysis. Overall, the system identified diabetic retinopathy requiring referral with a sensitivity of 96.9% and a specificity of 87.7%, and over 93% of participants were satisfied with the service. However, specificity was substantially lower among participants recruited from Aboriginal medical services, meaning that more people without the condition were wrongly flagged for referral. The study used clinic location as an approximation of Indigenous status.

The authors note that the AI system had been trained exclusively on images from Chinese adults, and that differences in retinal pigmentation may affect its accuracy. They also point to other possible factors, such as poorer image quality, older age and other eye conditions. These explanations were not tested in the study.

Learn more about this study here: https://doi.org/10.1038/s41598-021-94178-5

Reference:

Scheetz, J., Koca, D., McGuinness, M., Holloway, E., Tan, Z., Zhu, Z., O’Day, R., Sandhu, S., MacIsaac, R.J., Gilfillan, C., Turner, A., Keel, S., & He, M. (2021). Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia. Scientific Reports, 11, 15808.
Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis

AI models developed using data from very similar populations may not work as well for other groups, and could reflect or even worsen existing racial and ethnic disparities in health care. This study tested whether this was the case for an AI model that uses electrocardiograms (ECGs) to detect a weakened heart pumping function, known as low left ventricular ejection fraction.

The model had been developed with data from nearly 45,000 patients, 96.2% of whom were non-Hispanic White. When tested on a separate group of almost 53,000 patients, it performed consistently well across all the racial and ethnic groups analysed: non-Hispanic White, Asian, Black/African American, Hispanic/Latino and American Indian/Native Alaskan. A separate AI model was able to identify patients’ racial group from the ECG to some extent, showing that ECG characteristics do vary by race, but this did not affect the first model’s performance.

The authors conclude that, in this case, racial differences in the ECG did not affect the model’s ability to detect the condition. Even so, they recommend that the performance of all new AI tools in medicine be reported across different ethnic, racial, age and sex groups, to ensure they are used responsibly.

Learn more about this study here: https://doi.org/10.1161/CIRCEP.119.007988

Reference:

Noseworthy, P.A., Attia, Z.I., Brewer, L.C., Hayes, S.N., Yao, X., Kapa, S., Friedman, P.A., & Lopez-Jimenez, F. (2020). Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis. Circulation: Arrhythmia and Electrophysiology, 13(3), e007988.
Fairness And Bias Mitigation in AI Models for Diabetes Diagnosis: A Comparative Evaluation of Algorithmic Approaches

AI models that predict diabetes can produce biased results for certain groups of patients, and several methods have been developed to reduce this. This study compared three of these methods on the same data, a widely used dataset of 768 women aged 21 and over, assessing fairness across age groups.

Each method had different strengths. The first relied on simple, transparent decision rules, making its predictions easy to interpret, but it had no specific mechanism to ensure fairness. The second was designed to prevent the model from basing its predictions on age, and achieved perfect results on the fairness measures used while keeping accuracy at an acceptable level. The third corrected the imbalance between patients with and without diabetes in the data, which gave the highest overall accuracy but did not ensure fairness.

The authors conclude that none of the methods achieves the right balance between accuracy, fairness and interpretability on its own, and propose combining several strategies to reduce bias in AI-based diabetes diagnosis.

Learn more about this study here: https://doi.org/10.54554/jtec.2025.17.03.004

Reference:

Mohd Hamdan, M.D.H., Ab Jabal, M.F., Abdul Rahman, S., & Kapi, A.Y. (2025). Fairness And Bias Mitigation in AI Models for Diabetes Diagnosis: A Comparative Evaluation of Algorithmic Approaches. Journal of Telecommunication, Electronic and Computer Engineering, 17(3), 27–34.
Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images

AI can accurately diagnose coronary artery disease from myocardial perfusion imaging, a type of scan that shows blood flow to the heart muscle. However, these models are often trained on high-risk patients who were referred for invasive tests, which can lead them to overestimate the probability of disease in other patients. This study tested ways of training AI so that the probabilities it estimates match the real probability of disease more closely.

Deep learning models were trained on 828 patients from three sites and then tested on a separate group of 511 patients, including 179 with a low likelihood of disease. The best results came from a model trained with additional examples of patients without coronary artery disease and with little or no sign of reduced blood flow on the scan. It produced more accurate probability estimates than the model trained without these additional cases, and the improvement was most pronounced in women. It was also better at identifying disease than standard measures taken from the scans.

The authors conclude that adding more low-risk patients during training can improve how accurately AI estimates the probability of disease. This matters especially for lower-risk patients and for women, for whom overestimated probabilities could affect decisions about further tests and treatment.

Learn more about this study here: https://doi.org/10.1007/s00259-022-05972-w

Reference:

Miller, R.J.H., Singh, A., Otaki, Y., Tamarappoo, B.K., Kavanagh, P., Parekh, T., Hu, L.-H., Gransar, H., Sharir, T., Einstein, A.J., Fish, M.B., Ruddy, T.D., Kaufmann, P.A., Sinusas, A.J., Miller, E.J., Bateman, T.M., Dorbala, S., Di Carli, M.F., Liang, J.X., … Slomka, P.J. (2023). Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images. European Journal of Nuclear Medicine and Molecular Imaging, 50(2), 387–397.
Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction

Machine learning models are increasingly used to estimate a person’s risk of cardiovascular disease, but bias can be introduced when health data is collected and prepared, affecting how well these models work for certain groups. This study used electronic health records from 109,490 people treated at Vanderbilt University Medical Center, in the US, to test whether several machine learning models performed equally well across race and gender. It also compared them with a widely used risk equation from the American Heart Association that does not use machine learning.

Most of the machine learning models were less biased than the American Heart Association equation. Differences between racial groups were small, but differences between women and men were considerably larger. According to the authors, both the risk equation and the machine learning models were biased against women.

The researchers also tested three ways of reducing this bias. Removing race and gender from the data did not significantly reduce bias in most models, and balancing the number of people from each group in the training data did not consistently help either. Balancing the proportion of people with cardiovascular disease in each group reduced the gender bias, but not the racial bias, and slightly reduced accuracy in many cases. The authors conclude that gender disparities in cardiovascular risk prediction need to be investigated and corrected.

Learn more about this study here: https://doi.org/10.1016/j.jbi.2023.104294

Reference:

Li, F., Wu, P., Ong, H.H., Peterson, J.F., Wei, W.-Q., & Zhao, J. (2023). Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction. Journal of Biomedical Informatics, 138, 104294.
Depression among people with dyspepsia and H. pylori infection: A community based cross-sectional study in Ethiopia

A 2022 community-based study in southwest Ethiopia explored potential links between Helicobacter pylori infection, dyspepsia, and depression in residents aged 13 years and older.

Among the 871 participants – most of whom were married, had no formal education, and lived in rural areas – around 11% showed signs of probable depression. The prevalence was slightly higher (13%) among those experiencing at least one symptom of dyspepsia. Interestingly, in this study, H. pylori infection alone was not linked to higher depression rates, nor were age or gender.

The findings suggest digestive symptoms may increase the risk of depression, highlighting the importance of addressing both mental and gut health when managing gastrointestinal symptoms.

Learn more about this study here: https://doi.org/10.1371/journal.pone.0275424

Reference

Soboka, M., Gudina, E. K., Gashaw, M., Amare, H., Berhane, M., Desalegn, H., Tewolde, D., Jebena, M. G., Ali, S., Wieser, A., Froeschl, G., & Tesfaye, M. (2022). Depression among people with dyspepsia and H. pylori infection: A community based cross-sectional study in Ethiopia. PloS one, 17(10), e0275424.
Digital health tools for the passive monitoring of depression: a systematic review of methods

This systematic review examines studies linking passive data from smartphones and wearables to depression, identifying key methodological flaws and threats to reproducibility. It highlights biases such as representation, measurement, and evaluation bias, stemming from small, homogenous samples and inconsistent feature construction.

Although gender and race are not explicitly discussed, the lack of diversity in study populations suggests potential demographic bias. The review calls for improved reporting standards and broader sample inclusion to enhance generalizability and clinical relevance. These improvements are essential for ensuring that digital mental health tools are equitable and reliable across diverse populations.

Learn more about this review here: https://doi.org/10.1038/s41746-021-00548-8

Reference

De Angel, V., Lewis, S., White, K., Oetzmann, C., Leightley, D., Oprea, E., Lavelle, G., Matcham, F., Pace, A., Mohr, D. C., Dobson, R., & Hotopf, M. (2022). Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ digital medicine, 5(1), 3.
Rapamycin Attenuates Anxiety and Depressive Behavior Induced by Helicobacter pylori in Association with Reduced Circulating Levels of Ghrelin

In this experimental study, researchers investigated how H. pylori infection influences depression-like behavior and certain biological markers in mice, focusing especially on the hormone ghrelin.

Mice infected with the bacteria showed more anxiety- and depression-like behaviors in standard laboratory tests compared with healthy mice. They moved less, showed more signs of stress, and had lower preference for sweet solutions, a common indicator of loss of pleasure.
Biologically, H. pylori infection led to reduced circulating ghrelin levels and activation of the mTOR signaling pathway in the stomach. It was also associated with increased markers of brain inflammation and cell injury in the hippocampus, a brain region important for mood regulation. When mice were treated with rapamycin, an mTOR inhibitor, ghrelin levels increased and brain-inflammation markers were reduced.

Overall, the study suggests that H. pylori infection may contribute to anxiety- and depression-like behaviors by lowering ghrelin levels and increasing neuroinflammation, highlighting a potential biological link between gut infection and mental health.

Learn more about this study here: https://doi.org/10.1155/2022/2847672

Reference

Tian, Jiageng, Wang, Zeyu, Ren, Yadi, Jiang, Yong, Zhao, Ying, Li, Man, Zhang, Zhiguang, Rapamycin Attenuates Anxiety and Depressive Behavior Induced by Helicobacter pylori in Association with Reduced Circulating Levels of Ghrelin, Neural Plasticity, 2022, 2847672, 8 pages, 2022.
Bias Discovery in Machine Learning Models for Mental Health

This article examined how AI can unintentionally reproduce social and demographic biases when applied to mental health prediction. Using benzodiazepine prescriptions as a proxy for conditions such as depression and anxiety, a study analyzed machine learning models trained on patient data to identify systematic disparities.

It found that women are more frequently predicted to receive such treatments, reflecting gender bias, while the models perform less accurately for minority ethnic groups, indicating representation and evaluation bias. The AI models here are not used to prescribe drugs but rather to predict treatment likelihoods, revealing how bias in healthcare data can lead to inequitable AI performance in the context of depression-related care.

Learn more about the article here: https://doi.org/10.3390/info13050237

Reference

Mosteiro, P.J., Kuiper, J., Masthoff, J., Scheepers, F., & Spruit, M. (2022). Bias Discovery in Machine Learning Models for Mental Health. Inf., 13, 237.
Increased risk of short-term depressive disorder after Helicobacter pylori eradication: A population-based nested cohort study

A study using Taiwan’s National Health Insurance data found that antibiotic therapy for H. pylori in patients with peptic ulcer disease was linked to a short-term increase in psychiatrist-diagnosed depression within 30 days. Women and patients treated with clarithromycin were particularly at higher risk.

The researchers reported that the increased risk of depression after H. pylori eradication therapy may involve alterations in the brain-gut-microbiome axis induced by antibiotic treatment, as is it well known that antibiotics can change the gut microbial composition, metabolism, and function, thereby affecting human health and possibly contributing to the pathophysiology of depression.

Based on these findings, the authors recommend that clinicians should monitor mental health shortly after H. pylori eradication, as short-term depressive symptoms may occur and be easily overlooked.

Learn more about this study here: https://doi.org/10.1111/hel.12824

Reference

Tsai C-F, Chen M-H, Wang Y-P, et al. Increased risk of short-term depressive disorder after Helicobacter pylori eradication: A population-based nested cohort study. Helicobacter. 2021; 26:e12824.
GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression

A study of over 450,000 people in the UK Biobank identified 8 independent genes that affect stomach acid, gut movement, and the body’s response to infection, including susceptibility to Helicobacter pylori infection.

The study also explored connections between these gut conditions and depression, which often occurs alongside digestive problems, providing new insights into the complex interplay between gut health and mental well-being.

Learn more about this study here: https://doi.org/10.1038/s41467-021-21280-7

Reference

Wu, Y., Murray, G.K., Byrne, E.M. et al. GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression. Nat Commun 12, 1146 (2021)
Helicobacter pylori Infection Is Associated with Long-Term Cognitive Decline in Older Adults: A Two-Year Follow-Up Study

Helicobacter pylori infection is usually known for causing stomach problems, but it may also affect brain health. This research study published in 2023 followed 268 older adults with memory complaints for two years to see whether H. pylori infection was linked to cognitive decline.

While at the beginning of the study, people with and without H. pylori performed similarly on memory tests, over the two-year follow-up, those with a history of infection showed greater declines in their Mini-Mental State Examination (MMSE) scores. After taking into account age, sex, education, genetic risk factors and common medical conditions, H. pylori infection was still associated with a significantly higher risk of cognitive decline, with infected participants more likely to lose three or more MMSE points and showing a faster rate of decline over time.

These findings suggest that H. pylori infection may contribute to progressive cognitive deterioration in older adults with memory complaints and may be relevant in understanding pathways linking infection and dementia.

Learn more about this study here: https://doi.org/10.3233/JAD-221112

Reference

Wang J, Yu N-W, Wang D-Z, et al. Helicobacter pylori Infection Is Associated with Long-Term Cognitive Decline in Older Adults: A Two-Year Follow-Up Study. Journal of Alzheimer’s Disease. 2023;91(4):1351-1358.
Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure

AI models that analyse electrocardiograms (ECGs) could help identify people at high risk of heart failure early, and potentially reduce racial disparities in heart failure outcomes. But whether these models perform equally well for different groups of patients has been little studied. This study used ECGs from 326,518 patient encounters at Stanford Hospital to evaluate a model trained to predict heart failure within five years, comparing its performance by race, ethnicity, age and sex.

Overall, there were no significant differences in performance between racial groups, and performance declined with age. However, among patients aged 0 to 40, the model performed significantly worse for Black patients than for any other racial group, with the largest differences among young Black women.

The researchers tested several ways of reducing these disparities: including race, ethnicity, sex and age in the model, training separate models for each racial group, and training it on data with equal racial representation. None of these improved the disparities. Adjusting the model’s decision thresholds individually by race, age and sex did lead to substantial improvements. The authors warn that machine learning tools for heart failure risk perpetuating existing disparities, and suggest these individualised thresholds as one way to reduce them.

Learn more about this study here: https://doi.org/10.1161/CIRCHEARTFAILURE.123.010879

Reference:

Kaur, D., Hughes, J.W., Rogers, A.J., Kang, G., Narayan, S.M., Ashley, E.A., & Perez, M.V. (2024). Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure. Circulation: Heart Failure, 17(1), e010879.
Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups

Stroke is a leading cause of death and serious long-term disability in the US, with a particularly high risk among Black individuals. Risk prediction tools help identify who would benefit most from prevention, but they need to work equally well for everyone. This study compared several of these tools, including two stroke-specific models, a general cardiovascular risk model and two new machine learning models, using data from 62,482 Black and White adults aged 45 and over from four large US studies.

Overall, the models performed similarly, and the new machine learning models did not improve their accuracy. However, all of them were worse at distinguishing between people who would and would not have a stroke when applied to Black individuals. Among women, the models’ accuracy score was 0.76 for White women and 0.69 for Black women. Among men, it ranged from 0.71 to 0.72 for White men and from 0.64 to 0.66 for Black men. The models also performed better in adults under 60 than in older adults.

The authors conclude that current approaches are not enough to close this gap, and that the range of risk factors considered and the modelling techniques used need to be improved to address these racial disparities.

Learn more about this study here: https://doi.org/10.1001/jama.2022.24683

Reference:

Hong, C., Pencina, M.J., Wojdyla, D.M., Hall, J.L., Judd, S.E., Cary, M., Engelhard, M.M., Berchuck, S., Xian, Y., D’Agostino, R., Howard, G., Kissela, B., & Henao, R. (2023). Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups. JAMA, 329(4), 306–317.
Confounders mediate AI prediction of demographics in medical imaging

Previous research has suggested that AI can identify a patient’s age, sex and even race from medical images. This study tested whether this holds for echocardiograms, ultrasound videos of the heart, using more than 530,000 videos from two US medical centres, Cedars-Sinai and Stanford.

The models predicted sex well and age with an average error of about 9 years, and these results held across both centres. Race, however, could not be reliably predicted. When the researchers changed the balance of age or sex in the data used to train the race model, its performance varied widely, while changing the racial composition of the data had little effect on the predictions of sex and age.

The authors conclude that much of the model’s apparent ability to predict race comes from “shortcuts” through other features that differ between racial groups, rather than from race itself. They warn that treating AI predictions of race as evidence of biological differences risks reinforcing a false idea that has contributed to medical errors, since differences in health between racial groups often reflect social factors and structural racism. They call for further research into which imaging features carry demographic information, and into the risks of bias this creates in medical AI.

Learn more about this study here: https://doi.org/10.1038/s41746-022-00720-8

Reference:

Duffy, G., Clarke, S.L., Christensen, M., He, B., Yuan, N., Cheng, S., & Ouyang, D. (2022). Confounders mediate AI prediction of demographics in medical imaging. npj Digital Medicine, 5, 188.
Addressing Artificial Intelligence Bias in Retinal Diagnostics

Diabetic retinopathy, damage to the retina caused by diabetes, is one of the conditions in which AI is used to support diagnosis by analysing images of the back of the eye. This study looked at what happens when the data used to train these systems is unbalanced, and tested a way of reducing the resulting bias.

The researchers used a public dataset of retinal images to which clinicians had added skin-tone labels. To recreate a situation of imbalance, they removed from the training data the images of people with darker skin who had diabetic retinopathy requiring referral to a specialist, while keeping these images in the test data. The standard system was 73.0% accurate for people with lighter skin and 60.5% for people with darker skin, a gap of 12.5 percentage points.

They then trained the system with additional synthetic retinal images created by generative AI models. Accuracy became 72.0% for people with lighter skin and 71.5% for people with darker skin, reducing the gap to 0.5 points. The authors conclude that unbalanced training data can lead to unequal accuracy across groups, and that synthetic images may help reduce this bias in the diagnosis of diabetic retinopathy and possibly in other AI systems used in eye care.

Learn more about this study here: https://doi.org/10.1167/tvst.10.2.13

Reference:

Burlina, P., Joshi, N., Paul, W., Pacheco, K.D., & Bressler, N.M. (2021). Addressing Artificial Intelligence Bias in Retinal Diagnostics. Translational Vision Science & Technology, 10(2), 13.
Age, sex and race bias in automated arrhythmia detectors

Algorithms that read electrocardiograms (ECGs) automatically are increasingly used to help identify heart conditions, but few studies have examined whether they perform equally well for different groups of patients. This study compared 56 open-source algorithms, developed by participants in the 2021 PhysioNet Challenge, on more than 130,000 ECGs, grouping the results by sex, age and race.

Several performance measures were similar across groups, but others showed clear differences. Accuracy was significantly lower for Black patients and higher for Asian patients, with a gap of 39% on the test set. Accuracy also fell with age, by around 12% per decade, and results for male and female patients differed depending on the measure used.

The researchers then retrained one of the best-performing models with an additional constraint designed to reduce performance differences across sex, race and age. This led to a small loss in overall performance but a significant reduction in bias. The authors conclude that bias depends on the model’s design, the population it is trained on and how its performance is measured, and that all of these should be closely examined.

Learn more about this study here: https://doi.org/10.1016/j.jelectrocard.2022.07.007

Reference:

Perez Alday, E.A., Rad, A.B., Reyna, M.A., Sadr, N., Gu, A., Li, Q., Dumitru, M., Xue, J., Albert, D., Sameni, R., & Clifford, G.D. (2022). Age, sex and race bias in automated arrhythmia detectors. Journal of Electrocardiology, 74, 5–9.
Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases

Electrocardiography (ECG) enhanced by artificial intelligence is emerging as a promising tool for diagnosing and managing cardiovascular disease. This review explains how AI can detect subtle patterns in the ECG that human interpreters may miss, helping to identify problems such as arrhythmias and heart failure more quickly and consistently.

However, the authors also highlight important limitations, including systematic bias. When training datasets do not adequately represent different ages, sexes and racial groups, models can perform worse for those who are underrepresented, since ECG patterns vary with age, physiological differences between the sexes and genetic factors. To make these tools safe and fair in clinical practice, the review calls for more diverse datasets, bias mitigation strategies and continuous validation across populations.

Learn more about this review here: https://doi.org/10.1016/j.jelectrocard.2024.01.006

Reference:

Muzammil, M.A., Javid, S., Afridi, A.K., Siddineni, R., Shahabi, M., Haseeb, M., Fariha, F., Kumar, S., Zaveri, S., & Nashwan, A.J. (2024). Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. Journal of Electrocardiology, 83, 30–4
Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools

Cardiovascular disease remains the leading cause of death in women, and there is growing evidence of sex- and gender-based differences in how it develops and progresses. This review provides an overview of the main opportunities for cardiovascular screening in women and of the potential role of artificial intelligence and digital tools.

The authors note that healthcare providers, guideline committees and policymakers are not yet well equipped to collect data on women, interpret it, or use it to design targeted interventions. The review includes a section on bias in AI and looks at different stages of a woman’s life, from youth and pregnancy to menopause and older age, as well as the implications of AI for clinical guidelines and public health.

Learn more about this review here: https://doi.org/10.1161/CIRCRESAHA.121.319876

References:

Adedinsewo, D.A., Pollak, A.W., Phillips, S.D., Smith, T.L., Svatikova, A., Hayes, S.N., Mulvagh, S.L., Norris, C., Roger, V.L., Noseworthy, P.A., Yao, X., & Carter, R.E. (2022). Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools. Circulation Research, 130(4), 673–690.
Implicit gender bias in the diagnosis and treatment of type 2 diabetes: A randomized online study

This study examined whether implicit gender biases, unconscious associations about people based on their gender, influence how general practitioners (GPs) diagnose and treat type 2 diabetes. Ninety-nine GPs completed tests measuring two biases, one related to lifestyle (women having a healthier lifestyle than men) and one related to communication (men being less communicative than women), and then assessed clinical cases featuring a male or female patient.

Female GPs showed a significant lifestyle-related bias, while GPs of both genders showed a communication-related bias. The patient’s gender also affected clinical decisions: for example, GPs were less certain about the diabetes diagnosis when the patient was a woman.

These findings show how unconscious bias can shape clinical decision-making.

Learn more about this study here: https://doi.org/10.1111/dme.15087

Reference:

Skvortsova, A., Meeuwis, S.H., Vos, R.C., Vos, H.M.M., van Middendorp, H., Veldhuijzen, D.S., & Evers, A.W.M. (2023). Implicit gender bias in the diagnosis and treatment of type 2 diabetes: A randomized online study. Diabetic Medicine, 40(8), e15087
Mental Health, AI & Learning
Proposal: Using Artificial Intelligence tools to support and mediate learning for students with math related difficulties, preventing and minimizing mental health issues.

Proposed Implementation: 2026 to 2028

Call: HORIZON-CL2-2025-01 – Culture, Creativity and Inclusive Society – 2025

Proposed Budget: 3 850 702,80€

Keywords: Population dynamics, aging, health and society, Social sciences, interdisciplinary, Sociology

Objective: This interdisciplinary project brings together experts in AI, psychology, education, and policy across eight countries to understand how the timing and availability of support during digital learning influences mathematics anxiety, and to develop scalable, ethical solutions to address it.

The project will co-design, deploy, and evaluate AI model, a generative LM interactive system and will integrate foundational research, multi-country piloting, psychological analysis, and socio-economic modelling, supported by robust ethics, governance, and dissemination strategies. FAIR-aligned datasets and AI explainability tools will ensure transparency, privacy, and reuse.

Aligned with Horizon Europe Cluster 2 goals on educational resilience, mental health, and the Digital Education Action Plan, this project delivers new scientific insights, evidence-based policy recommendations, and an ethically grounded digital intervention with EU-wide relevance.

Partners:
- Liverpool John Moores University
- Health Citizens – European Institute
- Wageningen University
- Università Degli Studi Di Siena
- Finnovaregio
- SYNYO
- Kepez İlce Milli Egitim Mudurlugu
- Ibonis European Research Projects
- Fundación Cibervoluntarios
The association between psychological status and the development of early gastric cancer from atrophic gastritis

A recent hospital-based, cross-sectional observational study, was conducted in the Chinese population to explore the potential relationship between psychological state and the progression of atrophic gastritis (AG; caused by H. pylori or not) to early stage gastric cancer (EGC).

The study included 258 individuals receiving care in the Department of Gastroenterology at The Affiliated Hospital of Xuzhou Medical University between March 2020 and November 2024, and included 173 patients diagnosed with AG and 85 with EGC. Clinical profiles, demographic characteristics, and laboratory indices were initially recorded, and then comparative analyses were conducted between groups.

Results showed that, compared to the AG group, patients with EGC exhibited significantly higher psychological distress and depressive tendencies. These findings, while not conclusive, imply a possible association between psychological state and the presence of early gastric cancer (EGC) in patients with atrophic gastritis, thus suggesting that they could function as independent indicators and contribute to the malignant progression of EGC in patients.

Incorporating mental health screening tools could, therefore, offer supplementary value in the broader context of evaluating patients who might carry an increased likelihood of disease progression.

Learn more about this study here: http://dx.doi.org/10.1097/MD.0000000000045653

Reference

Liang, Mengmeng BD; Wang, Juan MD; Li, Rui MD; Yang, Jun MD; Liu, Yuping BD; Zhao, Lian BD. The association between psychological status and the development of early gastric cancer from atrophic gastritis. Medicine 104(45):p e45653, November 07, 2025
Scalable Soil Health Restoration
Proposal: Scalable Soil Health Restoration and Climate Resilience through Biostimulants, Modelling, and Community Networks

Proposed Implementation: 2026 to 2030

Call: HORIZON-MISS-2025-05 – Supporting the implementation of the Soil Deal for Europe Mission

Proposed Budget: 3 688 792,50€

Keywords: Soil improvement, Soil management, Soil science, ecological capsule system, Soil restoration, Salinity mitigation, Aridity resilience, Biochar engineering, Capsule material science, Digital ecological modelling.

Objective: The overarching objective is to develop, validate, and demonstrate the ecological capsules system, an integrated soil restoration technology addressing salinity, aridity, and land degradation challenges. The project combines advances in biochar engineering, capsule material science, digital ecological modelling, and socio-economic assessment into a coherent pathway that bridges laboratory innovation with field-scale demonstration and long-term exploitation.

Pilot trials will be implemented in three countries, in an area of around 100 000m², supported by harmonised monitoring protocols and FAIR-compliant data infrastructure. A predictive ecological Digital Twin will integrate field and laboratory data with socio-economic modelling to support decision-making and scaling. Outcomes include measurable improvements in soil organic carbon, salinity reduction, and biodiversity, as well as evidence-based adoption pathways aligned with EU Mission Soil KPIs. By linking technological innovation with policy-relevant impact assessment, the project directly contributes to the Mission Soil ambition of transitioning 75% of EU soils to health by 2030.

Partners:
- Panepistimio Dytikis Attikis
- Scyring, Inc
- Health Citizens – European Institute
- University of Glasgow
- Evenor Tech Sl
- Agrinewtech Srl
- Association Green Development and Innovation
- Cyens Centre of Excellence
- Periopsis Ltd
- Cerca Trova
- Circular Research Foundation Srl Impresa Social
- Minds & Sparks Gmbh
- Synyo Gmbh
- Tech2market Spolka Z Ograniczona Odpowiedzial
- Cluster for the Development of Start-Ups, Deep Tebg
Family Well-Being & Key Gender Drivers on Labour Market
Proposal: Advancing Labour Market Accessibility in the Mediterranean

Proposed Implementation: 2026 to 2029

Call: HORIZON-CL2-2025-02-TWO-STAGE – Culture, Creativity and Inclusive Society – 2025 – Two-stage

Proposed Budget: 3 200 000,00

Keywords: Gender in economics, Social economics, Women and gender studies, key gender drivers, labour market, parenthood, work-life balance, career opportunities, discrimination, capacity building actions, SMEs, key drivers, family well-being, qualitative research, EEN, SDG5

Objective: The ambition of the project is to provide evidence-based data, through a scientific and comparative in-depth study identifying key gender drivers on career outcomes among parents, analyzing how parenthood affects job opportunities, career growth and well-being and observing how these dynamics vary across countries and social groups, equipping policymakers at local, national, and EU levels with actionable insights and policy recommendations and practical tools.

Persistent gender inequalities in the labour market will be investigated in Mediterranean regions within specific target countries of the project, Italy, Spain, Portugal, Malta, Turkey and Cyprus, where domestic welfare models, cultural norms and labour market segmentation reinforce gender gaps. The impact of gender asymmetries on relational and psychological well-being in the private and family sphere will also be analysed.

The project adopts a multi-level approach: the analysis of existing EU data sources and national datasets, followed by a cross-country survey targeting working parents, a qualitative research phase showcasing case studies and capacity-building schemes addressed to SMEs.

Recent EU data show that the EU had 32.3 million enterprises, employing 160 million persons: micro and small enterprises account for 99%, employing 77.5 million persons, i.e. almost half of the total number of all employed. The EEN Network partners involved in the project have the unique position to reach these actors, whose voice cannot be ignored.

The project aligns with the EU Gender Equality Strategy 2020-25 and contributes to SDG5 and the European Pillar of Social Rights by promoting equal opportunities, work-life balance, and family well-being across Europe.

Partners:
- Consiglio Nazionale Delle Ricerche
- Health Citizens – European Institute
- Unione Per la Difesa dei Consumatori APS
- Federazione Trentina Della Cooperazione SC
- The Malta Chamber of Commerce and Enterprise AMT
- Csi Center for Social Innovation Ltd
- Passport 2 Employability International Foundation
- INESC TEC – Instituto de Engenharia de Sistemas e Comunicação
- Federación de Cooperativas y de la Economía Social
- EGE University
The association between H. pylori infection and cognitive deterioration: a systematic review and meta-analysis

The association between cognitive decline and H. pylori infection remains controversial, with some evidence suggesting that H. pylori eradication may slow the progression of the disease.

A new meta-analysis reviewed 16 studies to explore whether H. pylori affects cognitive function and whether cognitive decline is linked to higher rates of infection.

The analysis found that people with H. pylori infection had a higher risk of cognitive decline, especially when cognitive dysfunction and dementia were combined. However, the infection was not clearly linked to Alzheimer’s disease. Conversely, people with Alzheimer’s disease were more likely to have H. pylori infection than those without, though the association was weaker for other forms of dementia.

These findings suggest a bidirectional relationship in which H. pylori may contribute to cognitive decline, and certain cognitive conditions may increase susceptibility to infection. The study also highlights the need for more well-designed research to fully understand this complex interaction.

Learn more about this study here: https://doi.org/10.1186/s40001-025-03160-8

Reference

Elhady, M.M., Zidan, A., Rabea, E.M. et al. The association between H. pylori infection and cognitive deterioration: a systematic review and meta-analysis. Eur J Med Res 30, 846 (2025)
Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches

A study found that large language models (LLMs) outperform traditional deep neural network (DNN) embeddings in automated depression detection and show reduced gender bias, though racial disparities remain. Among DNN fairness-mitigation techniques, the worst-group loss provided the best balance between overall accuracy and demographic fairness, while fairness-regularized loss underperformed.

The identified biases affect the fairness and diagnostic reliability of AI systems for mental health assessment, particularly by disadvantaging underrepresented racial and gender groups, mainly Hispanic participants in the case of this research. Such disparities risk perpetuating inequities in automated mental health screening and could undermine trust and validity in clinical or public health applications.

Learn more about the study here: https://doi.org/10.48550/arXiv.2509.25795

Reference

Junias, O., Kini, P., & Chaspari, T. (2025). Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches. 2025 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 1-7.
Seniors Neuropsychological Disorders Patients & EU Fundamental Rights
Proposal: Safeguarding dementia & other neuropsychological disorders patients’ and caregivers’ rights through strategic litigation in Europe

Proposed Implementation: 2026 to 2029

Call: CERV-2025-CHAR-LITI – Call for proposals to promote civil society organisations’ awareness of, capacity building and implementation of the EU Charter of Fundamental Rights

Proposed Budget: 421 359,58€

Keywords: dementia, Strategic litigation, EU Charter, rights, neuropsychological disorders, seniors

Objective: Seniors with dementia or other neuropsychological disorders face some of the highest risks of rights violations in the EU, ranging from unequal access to services and healthcare, to neglect, abuse, and discrimination. Despite the protection offered by the EU Charter of fundamental rights, their rights often remain unrecognized or unenforced. Caregivers, who can play a crucial role in safeguarding these rights, frequently lack adequate knowledge and support to act as advocates. Most civil society organizations working with them have not yet used the potential of strategic litigation, thus missing an important opportunity to defend and promote their rights.

The project addresses this gap by empowering seniors with dementia or other neuropsychological disorders and their caregivers, while strengthening the capacity of civil society organizations, human rights defenders, legal professionals & practitioners, Ombuds Institutions, equality bodies & national human rights institutions to use the EU Charter for advocacy and strategic litigation.

The project’s impact will be threefold:

-Empowerment and dignity for seniors with dementia or other neuropsychological disorders, who will gain accessible tools (home toolkit) and dedicated support (rights advocators) to make their voices heard.

-Strengthened advocacy and protection by caregivers, volunteers, and professionals, who will act as “rights advocators” and ensure continuity beyond the project’s lifespan.

-Systemic change through capacity building of organizations, institutions & professionals across Europe, making rights enforcement a shared and ongoing responsibility.

By combining empowerment of individuals with structural capacity-building, the project will help transform awareness into action and action into systemic change, ensuring that the rights of seniors with dementia or other neuropsychological disorders are not only recognized in principle but fully enforced in practice across the EU.

Partners:
- Challedu Astiki Mi Kerdoskopiki Etaireia
- Health Citizens – European Institute
- Koinofeles Somateio Arogis Kai Frontidas Ilikiomeel
- Kentro Evropaikou Syntagmatikou Dikaio Idryma
- Erevnitiko Idrima
- Cooperativa Sociale Cooss Marche Onlus Societa
- Komiteen for Sundhedsoplysning
Migrant & Vulnerable Group’s Health
Proposal: A Transnational Gateway for Access, Training, and Empowerment in Migrant Healthcare

Proposed Implementation: 2026 to 2030

Call: AMIF-2025-TF2-AG-INTE – Transnational Actions on Asylum, Migration and Integration 2025

Proposed Budget: 1 779 118,96€

Keywords: migrant health, integration, healthcare access, well-being, accessibility, health literacy, vulnerable groups

Objective: Address the critical gap between migrants’ legal entitlement to healthcare services and their actual ability to access those services, according to the EU Action Plan on Integration and Inclusion (2021-2027) and the Pact on Asylum and Migration.

Project has been designed on a transnational, evidence-based and participatory methodology, in order to:
- increase migrants’ awareness regarding their healthcare rights and accessibility to relevant services,
- build the capacity of healthcare professionals and public officials to deliver inclusive, culturally competent and trauma-informed care
- raise awareness of the special health needs of vulnerable groups,
- foster transnational mutual learning and knowledge transfer by leveraging on existing tools and practices and
- advocate for policy change, on a systemic level, finally achieving equitable access to healthcare.
These objectives will be achieved via needs analysis which will indicate the relevant barriers. Through a co-design methodology, a series of comprehensive resources will be developed and exploited, all hosted on an accessible e-Health Learning Platform. These resources will be piloted and delivered through a series of capacity-building activities for multiple stakeholders, including migrants, healthcare professionals and public officials, resulting in a relevant policy recommendation.

The project mobilizes a multi-disciplinary partnership, across EU member-states with varying levels of health system inclusiveness, promoting cross-border collaboration and transfer of knowledge and practice. And will involve more than 3.600 migrants and 300 healthcare professionals and public officials in 6 countries.

Partners:
- Ethniko Kai Kapodistriako Panepistimio Athinon
- Health Citizens – European Institute
- Giatri Tou Kosmou Elliniki Antiprosopia
- Technologiko Panepistimio Kyprou
- Friedrich-Alexander-Universitaet Erlangen-Nuernde
- Camara Municipal de Lisboa
- Elliniko Forum Metanaston – EFM
- Athens Lifelong Learning Institute Astiki Mi Kerdoel
- Magnetar Ltd
- I-Skills A.E.
- Pricewaterhousecoopers Business Solutions AE
- Centrul Pentru Promovarea Învățării Permanentero
- Socialiniu Inovaciju Fondas
- Institouto Anaptixis Apasxolisis
Mental Health Literacy for Kids
Proposal: Empowering Children with Mental Health Literacy Online

Proposed Implementation: 2026 to 2028

Call: CERV-2025-CHILD – Rights of the child and children’s participation

Proposed Budget: 341 576,10€

Keywords: Child mental health, Prevent cyberbullying, media literacy, online safety

Objective: The project aims to address the pressing issue of mental health among adolescents aged 14-17 in the context of digitalization. It focuses on developing strategies to support teenagers’ mental well-being in the face of various challenges exacerbated by digitalization, such as cyberbullying, exposure to harmful content, and excessive screen time.

The initiative entails mapping good practices across four countries, bringing together psychological experts to develop training formats aimed at preparing the educational staff and youth workers to support children’s mental health, both within and beyond school environments.

After training sessions, each partner organization is responsible for conducting local activities with parents and children to raise awareness and provide support. The project emphasizes the importance of promoting digital and media literacy skills among children and youth to navigate the digital world safely and responsibly.

Through collaborative efforts with stakeholders, the project seeks to foster a supportive environment for children’s mental health and well-being in the digital age.

Partners:
- Asociatia Umanista Romana
- Health Citizens – European Institute
- Loesje Bitola
- Society and Enterprise Development Institute
Developing personalized algorithms for sensing mental health symptoms in daily life

This study investigates algorithmic bias in AI tools that predict depression risk using smartphone-sensed behavioral data.

It finds that these tools underperform in larger, more diverse populations because the behavioral patterns used to predict depression are inconsistent across demographic and socioeconomic subgroups.

Specifically, the AI models often misclassify individuals from certain groups—such as older adults or those from different racial or gender backgrounds—as being at lower risk than they actually are. The authors emphasize the need for tailored, subgroup-aware approaches to improve reliability and fairness in mental health prediction tools. This work highlights the importance of addressing demographic bias to ensure equitable AI deployment in mental healthcare.

Learn more about this study here: https://doi.org/10.1038/s44184-025-00147-5

Reference

Timmons, A.C., Tutul, A.A., Avramidis, K. et al. Developing personalized algorithms for sensing mental health symptoms in daily life. npj Mental Health Res 4, 34 (2025).
Regulatory Effects of Probiotics on Anxiety and Depression‑Like Behaviors in H. pylori‑Infected Rats

In a recent experimental study, researchers used rats to explore whether the use of probiotics such as Lactobacillus can mitigate anxiety- and depression-like behaviors, counteracting the psychological and biological effects of H. pylori infection. Infected rats were treated with each probiotic alone or with the combination, and were then evaluated using standard behavioral tests for anxiety and depression.

Both probiotics, especially when co-administered, reversed the depressive and anxiogenic effects induced by H. pylori. Probiotic supplementation also corrected several brain changes linked to H. pylori, including oxidative stress, inflammation, reduced BDNF/serotonin, and elevated corticosterone.

The findings suggest that multi-strain probiotics may help manage psychiatric symptoms associated with H. pylori infection, and that they merit further clinical evaluation in patients with psychiatric comorbidities.

Learn more about this study here: https://doi.org/10.1007/s12602-025-10674-4

Reference

Ahmadi-Soleimani, S.M., Masoudi, M., Tabrizi, A.M.A. et al. Regulatory Effects of Probiotics on Anxiety and Depression-Like Behaviors in H. pylori-Infected Rats. Probiotics & Antimicro. Prot. (2025)
Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models

The article investigates racial bias in psychiatric diagnosis and treatment recommendations across four large language models (LLMs): Claude, ChatGPT, Gemini, and NewMes-15. The study evaluates the models’ responses to ten psychiatric cases representing five diagnoses (depression, anxiety, schizophrenia, eating disorders, and ADHD) under three conditions: race-neutral, race-implied, and race-explicitly stated (African American).

Key findings include:

1) Bias in Treatment Recommendations: LLMs often proposed inferior or divergent treatments when racial characteristics were explicitly or implicitly indicated, particularly for schizophrenia and anxiety cases. Diagnostic decisions showed minimal bias overall.

2) Model Performance: NewMes-15 exhibited the highest degree of racial bias, while Gemini demonstrated the least bias across conditions.

3) Statistical Analysis: A Kruskal–Wallis H-test revealed significant differences in bias among the LLMs, with Gemini being significantly less biased than ChatGPT and NewMes-15.

4) Challenges in AI Development: The study highlights that LLMs trained on biased datasets may perpetuate racial disparities in psychiatric care, even when specialized medical training data is used. Local LLMs, despite their cost and privacy advantages, showed higher susceptibility to bias compared to larger, online models.

Learn more about this study here: https://doi.org/10.1038/s41746-025-01746-4

Reference

Bouguettaya, A., Stuart, E.M. & Aboujaoude, E. Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models. npj Digit. Med. 8, 332 (2025).
Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detection

A domain adversarial training (DAT) was developed in a study as a method to reduce gender bias in AI models for depression and PTSD detection using speech data (E-DAIC dataset).

DAT improved F1-scores up to +13% and reduced gender gaps in detection accuracy, improving generalization across male and female participants, specially addressing the effects of the latter’s underrepresentation.

Learn more about this study here: https://doi.org/10.48550/arXiv.2505.03359

Reference

Kim, J., Yoon, H., Oh, W., Jung, D., Yoon, S., Kim, D., Lee, D., Lee, S., & Yang, C. (2025). Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detection. 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1-7.
Genetic correlation, pleiotropic loci and shared risk genes between major depressive disorder and gastrointestinal tract disorders

Although depression is often linked to digestive disorders, the biological connection behind this phenomenon has remained unclear.

A recent genome-wide association study analyzed genetic data from hundreds of thousands of people to explore potential links between major depressive disorder (MDD) and gastrointestinal conditions such as peptic ulcers (mainly caused by H. pylori infection), acid reflux, irritable bowel disease, and inflammatory bowel disease.

The researchers found that depression shares genetic risk factors with most digestive disorders, meaning that some of the same genes and genetic regions influence both mental and gut health. The study also suggested that genetic susceptibility to certain gut conditions may increase the risk of depression, once again highlighting the strong gut-brain connection.

These findings could help scientists better understand the gut-brain connection and may point to new ways to treat gastrointestinal symptoms in patients with depression.

Learn more about this study here: https://doi.org/10.1016/j.jad.2025.01.048

Reference

Zhou, S., Zi, J., Hu, Y., Wang, X., Cheng, G., & Xiong, J. (2025). Genetic correlation, pleiotropic loci and shared risk genes between major depressive disorder and gastrointestinal tract disorders. Journal of affective disorders, 374, 84–90.
Minding the Gaps: Neuroethics, AI, and Depression

In this article, the author highlights the benefits and potential issues regarding the use of AI in depression diagnosis/treatment, focusing on the prevalent gender, racial and ethnicity biases.

It is mentioned that, given the historical, inherent biases in society generally and healthcare specifically, AI-driven advancements are not going to serve minority groups as a matter of course. Unless they are tailored to represent and serve all communities equally, they will exacerbate existing biases and disparities.

Learn more about this article here: https://nonprofitquarterly.org/minding-the-gaps-neuroethics-ai-and-depression/

Reference

Boothroyd, Gemma (2024), “Minding the Gaps: Neuroethics, AI, and Depression”, in Nonprofit Quarterly Magazine, winter 2024, “Health Justice in the Digital Age: Can We Harness AI for Good?”
Bias and Fairness in AI-Based Mental Health Models

The paper examines bias and fairness issues in AI-based mental health applications, including diagnostic tools, chatbots, and suicide risk prediction models. It reports how unrepresentative datasets lead to misdiagnosis and unequal outcomes across different socioeconomic, gender and racial groups – namely concerning women, local ethnic minorities or non-Western societies -, and presents mitigation strategies such as diverse datasets, fairness metrics, and human-in-the-loop approaches.

Learn more about this paper here: https://www.researchgate.net/publication/389214235_Bias_and_Fairness_in_AI-Based_Mental_Health_Models

Reference

Barnty, Barnabas & Joseph, Oloyede & Ok, Emmanuel. (2025). Bias and Fairness in AI-Based Mental Health Models.
AI and Mental Healthcare – ethical and regulatory considerations

This governmental report discusses the ethical and regulatory considerations of using artificial intelligence in mental healthcare in the UK.

Bias in AI tools (algorithmic bias) can stem from various places, including tools being trained on biased datasets and outputting discriminatory outcomes or developers making biased decisions in the design or training of such tools. For example, mental health Electronic health record (EHR) data is susceptible to cohort and label bias. This can occur because culture-bound presentations of mental disorders, combined with a lack of transcultural literacy among clinicians, often lead to both over- and under-diagnosis. People can also exhibit bias when using AI tools, such as over-relying on, or mistrusting AI outputs. All these biases can be conscious or unconscious.

Learn more about the report here: https://doi.org/10.58248/PN738

Reference

Gardiner, Hannah and Natasha Mutebi (2025), AI and Mental Healthcare – ethical and regulatory considerations, UK Parliament – POST, POSTnote 738, 31 January 2025
A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

This study examines classification parity across sex and finds that female adolescents have systematically under-diagnosed mental health disorders: their model’s accuracy was ~4 % lower and false negative rate ~9 % higher compared to male patients. The source of the bias resides in the textual data, namely notes corresponding to male patients tended to be on average 500 words longer and had distinct word usage. To mitigate this, the authors introduce a de-biasing method, based on neutralizing biased terms (gendered words and pronouns) and reducing sentences to essential clinical information. After correcting, diagnostic bias is reduced by up to 27%.

This emphasizes how linguistically transmitted bias—ensuing from word choice and gendered language—consistently leads to the under-diagnosis of mental health disorders among female adolescents, which critically undermines the impartiality of medical diagnosis and treatment.

Learn more about this study here: https://doi.org/10.48550/arXiv.2501.00129

Reference

Ive, J., Bondaronek, P., Yadav, V., Santel, D., Glauser, T., Cheng, T., Strawn, J.R., Agasthya, G., Tschida, J., Choo, S., Chandrashekar, M., Kapadia, A.J., & Pestian, J.P. (2024). A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection.
Neuropsychological Disorders, Seniors and Fundamental Rights
Proposal: Strategic Litigation to Address Seniors Rights in European Union

Proposed Implementation: 2025 to 2027

Call: CERV-2024-CHAR-LITI – Promote civil society organisations’ awareness of, capacity building and implementation of the EU Charter of Fundamental Rights

Proposed Budget: 508 057,40€

Keywords: Strategic Litigation, Seniors, Neuropsychological disorders, Dementia, civil society organizations, capacity building, legal professionals and practitioners, human rights defenders, EU Charter

Objective: the project aims to promote Strategic Litigation actions and enforcement of the EU Charter of Fundamental Rights to address breaches of the rights of Seniors and especially those with neuropsychological disorders. The project intends to form a community of practice to share knowledge and improve the multistakeholder cooperation and exchange of good practices among civil society organizations , human rights defenders, legal professionals and practitioners, Ombuds Institutions, equality bodies and independent human rights bodies.

The project also wants to develop tools on how to use EU Charter for strategic litigation of Seniors, developing the organizations ability to develop a litigation strategy and communicate and advocate on fundamental rights of Seniors under the Charter. As well as conducting research on case studies, and creating an advocacy campaign with short videos and podcasts on social media workshops increasing the awareness and knowledge of the key target groups and general public of seniors’ rights under EU law.

Partners:
- Challedu
- Health Citizens – European Institute
- Corporation for Succor and Care of Elderly and Disabled
- Kentro Evropaikou Syntagmatikou Dikaio Idryma
- Erevnitiko Idrima
- Cooperativa Sociale Cooss Marche Onlus Societa
Children’s Mental Health Literacy
Proposal: Empowering Children with Mental Health Literacy Online

Proposed Implementation: 2025 to 2027

Call: CERV-2024-CHILD – Rights of the child and children’s participation

Proposed Budget: 339 427,14€

Keywords: Mental Health, Raising awareness of children and/or young people

Objective: The project aims to address the pressing issue of mental health among adolescents aged 14-17 in the context of digitalization. It focuses on developing strategies to support teenagers’ mental well-being in the face of various challenges exacerbated by digitalization, such as cyberbullying, exposure to harmful content, and excessive screen time.

The initiative entails mapping good practices across four countries, bringing together psychological experts to develop training formats aimed at preparing the educational staff and youth workers to support children’s mental health, both within and beyond school environments.

The project emphasizes the importance of promoting digital and media literacy skills among children and youth to navigate the digital world safely and responsibly. Through collaborative efforts with stakeholders, the project seeks to foster a supportive environment for children’s mental health and well-being in the digital age.

Partners:
- Asociatia Umanista Romana
- Health Citizens – European Institute
- Loesje Bitola
- Society and Enterprise Development Institute
The Role of Gender: Gender Fairness in the Detection of Depression Symptoms on Social Media

The study found that the BDI-Sen dataset used for depression symptom detection on social media exhibits gender bias, with machine learning models such as mentalBERT showing predictive disparities that generally favour male users. Although bias mitigation techniques like data augmentation reduced the bias, they did not eliminate it completely.

The existence of this bias affects the fairness and reliability of AI systems in detecting depression symptoms, leading to unequal predictive performance across genders. This can result in under- or over-identification of depression symptoms in certain groups, thereby compromising the validity of such systems for clinical or mental health monitoring.

Learn more about this study here: https://studenttheses.uu.nl/handle/20.500.12932/47734

Reference

Gierschmann, Lara (2024), The Role of Gender: Gender Fairness in the Detection of Depression Symptoms on Social Media, Utrecht University, unpublished Master Thesis
AEQUITAS
Proposal: Preparation of the CSOs and public healthcare sector to address gender and racial biases that might arise from the wide usage of AI in order to protect and promote fundamental rights

Implementation: 2025 to 2027

Call: CERV-2024-CHAR-LITI – Promote civil society organizations’ awareness of, capacity building and implementation of the EU Charter of Fundamental Rights

Proposed Budget: 1 061 761,00€

Keywords: gender and racial biases, biomedical AI, EU Charter of Fundamental Rights

Objective: The topic of the AEQUITAS project is the gender and racial biases that have been reported in biomedical AI and which can lead to misdiagnosis and mistreatment and how they pose a threat to the fundamental rights protected by the EU Charter. The aim of the project is 3-fold:

– to increase the capacity of the CSOs and human rights organizations in educating the public, monitoring the biases and advocating for the protection of the fundamental rights especially regarding the biomedical AI;

– to increase the knowledge of healthcare staff from public hospitals on the biases that biomedical AI can present due to the biased data from which they were fed and to help them approach and consult these AI systems with a critical mindset;

– to develop an AI Regulatory Model that will be used by CSOs and public hospitals in their practices;

– to develop a European network of CSOs and public hospitals that will collaborate and support each other in the effort to raise public’s awareness on the gender and racial biases of biomedical AI and on the applications of the EU Charter;

– to raise awareness of the EU Charter of fundamental rights and its application in the AI era;

– to develop and distribute policy recommendations in order to advocate for the need to regulate biomedical AI.

Partners:
- Innovation Hive – Kypseli Kainotomias
- Kentro Ginaikeion Meleton Kai Ereyvnon Astiki Mi K
- Universitat Zu Koln
- Center for the Study of Democracy
- C.I.P. Citizens in Power
- Moterų Informacijos Centras Asociacija Mic
- Lobby Europeo de Mujeres en Espana LEM España
- TIA Formazione Internazionale Associazione APS
- Health Citizens – European Research Institute
- Technologiko Panepistimio Kyprou
- Cyens Centre of Excellence
- Edex – Educational Excellence Corporation Limited
- Rite Research Institute for Technological Evolution
- Vucable
- TotalEU Production
Project Website: https://aequitasproject.eu/
Association of Helicobacter pylori Infection with Depression and Anxiety: A Systematic Review and Meta-Analysis

The association between Helicobacter pylori infection and depression and anxiety has been reported in the literature.

A meta-analysis was developed in 2024 with the aim of investigating the association between H. pylori infection with these mental health conditions. The systematic search was conducted not only in international sources such as PubMed, Web of Science, and Embase, but also in Chinese databases, and looked for observational studies that reported the incidence or prevalence of depression and anxiety in patients with H. pylori infection.

Surprisingly, while the findings of this analysis showed a significant positive association between the bacteria and anxiety disorders, the association with depression appeared to be insignificant. Nevertheless, this finding seems to imply that clinicians treating H. pylori patients should also address their psychological well-being.

Learn more about this review here: https://doi.org/10.1155/2024/9247586

Reference

Li, Lu, Ren, Yadi, Wang, Zeyu, Niu, Yanqing, Zhao, Ying, Aihaiti, Xiaherezhati, Ji, Yinglan, Li, Man, Association of Helicobacter pylori Infection with Depression and Anxiety: A Systematic Review and Meta-Analysis, International Journal of Clinical Practice, 2024, 9247586, 9 pages, 2024.
Impact of Helicobacter pylori eradication on age‑specific risk of incident dementia in patients with peptic ulcer disease: a nationwide population‑based cohort study

A large South Korean cohort study from 2024 examined whether peptic ulcer disease (PUD) and Helicobacter pylori eradication therapy influence dementia risk in adults aged 55–79.

Using national health insurance data from 2002–2015 and propensity score matching, researchers assessed overall dementia and Alzheimer’s disease (AD) over 5–10 years. While the researchers did not directly verify the presence of the bacteria, their findings were based on treatment history.

The results showed that PUD was associated with a higher risk of developing dementia, with a stronger link for overall dementia than for AD. Eradication therapy itself did not markedly change overall risk, but later treatment was associated with greater dementia risk, highlighting the importance of timely management. Age-stratified analyses also indicated elevated AD risk, particularly in individuals in their 60s and 70s.

Overall, the findings suggest that PUD is a risk factor for dementia in older adults, and that early treatment of H. pylori infection may play a role in prevention strategies for neurodegenerative diseases.

Learn more about this study here: https://doi.org/10.1007/s11357-024-01284-z

Reference

Kang, D.W., Lee, JW., Park, M.Y. et al. Impact of Helicobacter pylori eradication on age-specific risk of incident dementia in patients with peptic ulcer disease: a nationwide population-based cohort study. GeroScience 47, 1161–1174 (2025).
Enhance Antimicrobial Resistance Diagnostic Methods
Proposal: Genetic Biomarkers and Bioactive Compounds (from Plants, Bacteria, and Fungi): Enhancing Diagnostic Methods with Innovative Solutions in Healthcare Settings Related to the Growing Enhance Antimicrobial Resistance

Proposed Implementation: 2025 to 2029

Call: HORIZON-HLTH-2024-DISEASE-09 – Partnerships in Health 2024

Proposed Budget: 36 862 500,00€

Keywords: Antimicrobial resistance, Antimicrobials, Bacteria, Fungi, Health determinants, Pathogen agents, Genetic biomarkers, bioactive compounds, diagnostics

Objective: This project aims to enhance antimicrobial resistance diagnostic methods by integrating genetic biomarkers and bioactive compounds derived from plants, bacteria, and fungi. The primary objectives include advancing diagnostic precision through the identification and validation of resistance genes, utilizing bioactive compounds to improve diagnostic assays, and developing novel diagnostic tools for antimicrobial resistance detection. These solutions will be implemented in both clinical and health settings to enable proactive monitoring and timely interventions.

To achieve these objectives, the project will develop new diagnostic assays, prototype innovative biosensors, and conduct clinical trials to validate the efficacy of these methods in real-world scenarios. The training of healthcare professionals and the creation of educational resources will further support the integration of these solutions into clinical practice. The project will also focus on ensuring the cost-effectiveness and scalability of these innovations, aligning with public health strategies for antimicrobial resistance management.

This project aligns with the work programme by addressing the need for improved diagnostic tools to combat antimicrobial resistance. It contributes to the scientific community by advancing research on biomarkers and bioactive compounds, offering practical solutions to enhance diagnostic accuracy and support personalized treatment strategies.

Partners:
- İzmir Project Agency Association
- Health Citizens – European Institute
- Azerbaijan Ministry of Science and Education Institute of Microbiology
- Baku State University
- The Stephan Angeloff Institute of Microbiology
- Fundació Hitt
- Istanbul Nisantasi University
- Altınbaş Üniversitesi
- İzmir Demokrasi Üniversitesi
- University of Zagreb
- University of Oradea
Predicting Environmental Impact on Health
Proposal: Predicting and Informing Air and Noise Pollution and Their Impact on Health from Environmental Data

Proposed Implementation: 2025 to 2030

Call: HORIZON-MISS-2024-CIT-01- Changing urban spaces and mindsets to accelerate the transition to climate neutrality

Proposed Budget: 4 998 382,50€

Keywords: Environmental health

Objective: The project aims to tackle the interconnected challenges of air and noise pollution and their significant health impacts through an innovative, data-driven, and participatory approach. By integrating advanced technologies such as IoT sensors, satellite data, and citizen-enabled data collection, the project builds a dynamic knowledge graph that maps the complex relationships between environmental factors and public health. This graph serves as a foundation for actionable insights, enabling stakeholders to make informed decisions and advocate for evidence-based policies.

The project emphasizes stakeholder engagement, fostering collaboration among policymakers, urban planners, public health officials, and citizens through workshops, training sessions, and capacity-building initiatives. The project also focuses on semantic enrichment and knowledge graph evolution, ensuring the system remains scalable, accurate, and adaptable to new data.

Through policy briefs, white papers, and advocacy efforts, the project bridges the gap between scientific research and practical applications, promoting sustainable practices and healthier communities. The combination of cutting-edge technology with participatory approaches in a multidisciplinary context, aims to drive meaningful change at local, national, and international levels, ultimately improving environmental quality and public health outcomes and will thus actively contribute to the Zero Pollution Action plan and the achievement of Climate-neutrality.

Partners:
- Université de Bordeaux
- Health Citizens – European Institute
- Centre Scientifique et Technique du Bâtiment
- Bordeaux Metropole
- Kauno Technologijos Universitetas
- Fondation de Lutte Contre le Diabète et les Maladies Non
- University of Labe
- Universite Cheikh Anta Diop De Dakar
- Tiga Bilgi Teknolojileri Anonim Şirketi
- İzmir İnovasyon Ve Teknoloji
- Innovathink Muhendislik San Tic As
- Dai Hoc Y Khoa Pham Ngoc Thach
- University of Waterloo
- Agence Universitaire de la Francophonie – Union E
- Institut National de la Santé et de la Recherche
- Universite Assane Seck de Ziguinchor
Gender Bias in AI’s Perception of Cardiovascular Risk

The study investigated gender bias in GPT-4’s assessment of coronary artery disease risk and showed that there was a substantial shift in the perception of risk between men and women when a psychiatric comorbidity was added to the vignette, even when they presented identical complaints.

This resulted in women being assessed as having lower risk of CAD when concurrently having a psychiatric condition.

Learn more about this study here: https://www.jmir.org/2024/1/e54242

Reference

Achtari M, Salihu A, Muller O, Abbé E, Clair C, Schwarz J, Fournier S Gender Bias in AI’s Perception of Cardiovascular Risk J Med Internet Res 2024;26:e54242 DOI: 10.2196/54242
Fairness in AI-Based Mental Health: Clinician Perspectives and Bias Mitigation

Considering how there is limited research on fairness in automated decision making systems in the clinical domain, particularly in the mental health domain, this study explores clinicians’ perceptions of AI fairness through two distinct scenarios: violence risk assessment and depression phenotype recognition using textual clinical notes.

Clinicians were engaged with through semi-structured interviews to understand their fairness perceptions and to identify appropriate quantitative fairness objectives for these scenarios. Then, a set of bias mitigation strategies were compared, developed to improve at least one of the four selected fairness objectives. The findings underscore the importance of carefully selecting fairness measures, as prioritizing less relevant measures can have a detrimental rather than a beneficial effect on model behavior in real-world clinical use.

Learn more about the article here: https://doi.org/10.1609/aies.v7i1.31732

Reference

Sogancioglu, G., Mosteiro, P., Salah, A. A., Scheepers, F., & Kaya, H. (2024). Fairness in AI-Based Mental Health: Clinician Perspectives and Bias Mitigation. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7(1), 1390-1400.
Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121

Depression and anxiety are common, often co-occurring mental health disorders that complicate diagnosis due to overlapping symptoms and reliance on subjective assessments.

Standard diagnostic tools are widely used but can introduce bias, as they depend on self-reported symptoms and clinician interpretation, which vary across individuals. These methods also fail to account for neurobiological factors such as neurotransmitter imbalances and altered brain connectivity.

Similarly, clinical AI/ML models used in healthcare often lack demographic diversity in their training data, with most studies failing to report race and gender, leading to biased outputs and reduced fairness. EEG offers a promising, objective approach to monitoring brain activity, potentially improving diagnostic accuracy and helping address biases in mental health assessment, as this study found.

Learn more about it here: https://doi.org/10.3390/brainsci14101018

Reference

Yousufi, M., Damaševičius, R., & Maskeliūnas, R. (2024). Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121. Brain sciences, 14(10), 1018.
Deconstructing demographic bias in speech-based machine learning models for digital health

This study explores gender and race bias in machine learning models that use speech to detect mental health outcomes, namely anxiety and depression. The authors examined where bias can come from, both in the acoustic features extracted from speech and in the labels used to train the models, as well as in the models’ decisions.

They also tested two ways of reducing bias: using only the speech features that carry the least demographic information, and transforming the data to remove traces of demographic information while keeping what matters for detecting the mental health condition. Performance varied across demographic groups, and the results of these approaches were mixed, highlighting the need for careful design so that speech-based tools work well for everyone.

Learn more about this study here: https://doi.org/10.3389/fdgth.2024.1351637

Reference

Yang M, El-Attar AA and Chaspari T (2024) Deconstructing demographic bias in speech-based machine learning models for digital health. Front. Digit. Health 6: 1351637.
Investigating the synergistic effects of amitriptyline and H. pylori eradication on depressive-like behaviors and inflammatory cytokines in mice

Recent research has highlighted the potential role of Helicobacter pylori in the pathogenesis of psychiatric disorders. In this experimental study, researchers used mice to explore whether combining amitriptyline with H. pylori eradication therapy produced greater benefits than either treatment alone.

Male mice were firstly allocated into four groups: healthy controls, H. pylori-infected mice, mice receiving antidepressant treatment only, and mice receiving both antidepressant and eradication therapy.

The initial findings confirmed that H. pylori infection induced depression-like behaviors in mice. In addition, while the use of antidepressants alone slightly improved these depression-like behaviors, it was the combination of antidepressants and bacterial eradication therapy that induced a more significant improvement in these symptoms. The combined treatment also appears to have led to an improvement in the psychomotor function of the animals.

Inflammatory cytokines (e.g., interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α)) are usually released in response to Helicobacter colonization and tissue damage, leading to a chronic state of inflammation, but are also associated with depression-like behaviors in humans and animal models, behaviors that have been shown to be attenuated by the use of antidepressants. In this study, researchers found that not only was H. pylori infection associated with changes in inflammatory cytokine levels, but that these alterations were partially reversed when both therapies were used together.

Overall, the findings suggest that targeting both infection and mood symptoms simultaneously may offer enhanced behavioral and anti-inflammatory benefits compared to isolated treatment approaches.

Learn more about this study here: https://doi.org/10.1016/j.physbeh.2024.114552

Reference

Miao Xu and Hua Fan, “Investigating the synergistic effects of amitriptyline and H. pylori eradication on depressive-like behaviors and inflammatory cytokines in mice”, Physiology & Behavior, Volume 281, 1 July 2024, 114552
Key language markers of depression on social media depend on race

A recent U.S. study published in PNAS found that artificial intelligence models analyzing social media posts can detect signs of depression in white Americans but are far less accurate for Black Americans, underscoring the dangers of using AI trained on non-diverse data in healthcare.

According to co-author Sharath Chandra Guntuku from Penn Medicine, these differences suggest that prior AI models and language-based assessments have largely overlooked racial diversity. While the researchers noted that social media analysis should not be used for diagnosis, it may still help assess risk or monitor mental health trends in communities

Learn more about the study here: https://www.pnas.org/doi/10.1073/pnas.2319837121

Reference

S. Rai et al (2024), Key language markers of depression on social media depend on race, Proc. Natl. Acad. Sci. U.S.A. 121 (14)
Artificial Intelligence in mental health and the biases of language based models

In this literature review of the uses of Natural Language Processing (NLP) models in psychiatry, an approach that “systematically evaluates each stage of model development to explore how biases arise from a clinical, data science and linguistic perspective” was employed to find existing patterns.

The result was that significant biases were found, with respect to religion, race, gender, nationality, sexuality and age.

Learn more about this review here: https://doi.org/10.1371/journal.pone.0240376

Reference

Straw, I., & Callison-Burch, C. (2020). Artificial Intelligence in mental health and the biases of language based models. PloS one, 15(12), e0240376.
The Psychotic Impact of Helicobacter pylori Gastritis and Functional Dyspepsia on Depression: A Systematic Review

The clinical practice of adding antidepressant drugs to the therapy for the eradication of Helicobacter pylori, in addition to the standard drug regimen, has been widely considered in recent years but its specific role in this treatment is still unclear.

In this systematic review researchers tried to establish if there is an association between H. pylori gastritis and depression and to further analyze the therapeutic effect of antidepressants on symptomatic relief in gastritis. For that, they analyzed randomized clinical trials, cross-sectional and prospective studies, and review articles that examined H. pylori infection, depression, functional dyspepsia, and antidepressant treatment. They focused especially on patients whose digestive symptoms did not improve even after successful H. pylori eradication.

Across the studies, a clear pattern emerged: patients with ongoing functional dyspepsia after H. pylori treatment often improved when they were given antidepressants, even when standard eradication therapy alone had not worked. However, the authors highlight that more research is needed before this approach becomes routine medical practice.

Learn more about this review here: https://doi.org/10.7759/cureus.5956

Reference

Al Quraan A M, Beriwal N, Sangay P, et al. (October 21, 2019) The Psychotic Impact of Helicobacter pylori Gastritis and Functional Dyspepsia on Depression: A Systematic Review. Cureus 11(10)
Psychological effects of Helicobacter pylori-associated atrophic gastritis in patients under 50 years: A cross-sectional study

A cross-sectional, observational study involving 975 Japanese individuals who underwent a health checkup, has found that people with atrophic gastritis had a significantly higher risk of experiencing psychological distress or depressed mood.

Interestingly, the risk was higher in females under 50 years old, regardless of H. pylori infection status.

Although the mechanism remains to be elucidated, the researchers suggest there is a possibility that nutritional status, neuroendocrinologic factors, and/or socioeconomic factors are involved. However, further studies are necessary to reveal the precise underlying mechanisms.

Learn more about this study here: https://doi.org/10.1111/hel.12445

Reference

Takeoka A, Tayama J, Kobayashi M, et al. Psychological effects of Helicobacter pylori-associated atrophic gastritis in patients under 50 years: A cross-sectional study. Helicobacter. 2017; 22:e12445
Correlation between social factors and anxiety-depression in function dyspepsia: do relationships exist?

A research study conducted on the Chinese population in 2014 aimed at evaluating the prevalence and the social factors linked to anxiety and depression in patients with functional dyspepsia (FD). This study included 907 patients with FD who attended a gastroenterology service.

Despite being a hospital-based study, results showed that patients with functional dyspepsia had higher anxiety and depression scores when compared to data from the general population. They also found that a higher prevalence of A/D was observed in women, older individuals, those with lower socioeconomic status (lower wages, lower education levels), and those with more stressful jobs, making these aspects risk factors for the development of A/D. Interestingly, they found no differences in relation to family history.

Learn more about this study here: https://doi.org/10.5114/pg.2014.47897

Reference

Huang, Z., Yang, X., Lan, L., Liu, T., Liu, C., & Li, J. et al. (2014). Correlation between social factors and anxiety-depression in function dyspepsia: do relationships exist?. Gastroenterology Review/Przegląd Gastroenterologiczny, 9(6), 348-353.