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Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models
Large multi-modal models (LMMs) are a fast-growing type of generative AI that can take in different types of data, such as text, images and video, and produce varied responses. The World Health Organization (WHO) has issued guidance on their use in health, with over 40 recommendations for governments, technology companies and healthcare providers. The guidance…
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AI-based diabetes care: risk prediction models and implementation concerns
AI is increasingly being used to predict a person’s risk of developing type 2 diabetes, which could help delay or prevent the disease through earlier, more personalised prevention. This editorial discusses a scoping review of 40 studies on AI models for diabetes risk prediction. It found that models combining electronic health records with other types…
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UnitedHealth lawsuit over alleged AI-driven denials of care moves forward
In November 2023, the families of two deceased patients filed a proposed class action lawsuit in the US against UnitedHealth Group, one of the country’s largest health insurers, and its subsidiary naviHealth. They allege the companies used an AI tool called nH Predict to wrongfully deny elderly patients in Medicare Advantage plans coverage for care…
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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…
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Predicting cardiovascular events from routine mammograms using machine learning
Cardiovascular risk is often underassessed in women, and many women have screening mammograms in midlife, just when their risk of cardiovascular disease starts to rise. Researchers led by The George Institute for Global Health developed an AI model that predicts a woman’s risk of a major cardiovascular event using only routine mammogram images and her…
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Can AI fight sex and gender bias in healthcare?
AI is transforming medicine, but it is mostly developed by men and trained on data that prioritises men’s bodies and health needs. In this commentary, researchers from the George Institute for Global Health argue that this embeds sex and gender biases that can put women and nonbinary patients at risk. For example, natural language processing…
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Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
AI systems now match expert performance in many medical imaging tasks, but there is growing concern that they may reflect and amplify human bias. This is especially worrying in the case of underdiagnosis, when AI wrongly labels a person who has a disease as healthy, which can delay their access to care. This study examined…
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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…
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Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review
AI models in primary care can help identify quickly and accurately the people who should receive care, but they risk perpetuating or amplifying biases against diverse groups. This scoping review looked at the strategies and methods used to reduce bias in primary care AI models, which groups were considered, and how these strategies affected both…
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Addressing AI Algorithmic Bias in Health Care
Biased AI algorithms can result in some groups of patients not receiving appropriate care, potentially leading to significant harm. In one example, an algorithm designed to identify patients who needed extra care used healthcare costs to estimate their needs. Because Black patients face barriers in accessing care, less was spent on them, and the algorithm…
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Enhancing Fairness and Accuracy in Diagnosing Type 2 Diabetes in Young Adult Population
Type 2 diabetes is most common in older people, but it is becoming more frequent among young adults, who face a higher risk of early and severe complications. Missing the diagnosis in this group can have serious consequences for their health. This study found that common machine learning models show what the authors call “digital…
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Inteligência Artificial no setor da saúde: desafios jurídicos e regulação
Artificial Intelligence in the health sector: legal challenges and regulation AI can significantly improve the quality of health services, but its use also raises well-founded concerns. This paper, published by the Office for Strategy and Studies (GEE) of the Portuguese Ministry of Economy, analyses the current landscape of AI in the Portuguese health sector and…
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Sociodemographic biases in medical decision making by large language models
Large language models (LLMs), such as the AI behind popular chatbots, show promise in healthcare, but they may make recommendations influenced by patients’ sociodemographic characteristics. Researchers tested nine LLMs on 1,000 emergency department cases, half real and half synthetic. Each case was presented in 32 versions: one for each of 31 sociodemographic groups, plus a…
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Addressing bias in big data and AI for health care: A call for open science
Most AI tools in healthcare learn from large datasets, but many groups of people have long been missing or misrepresented in medical data. When AI is trained on data that does not reflect the whole population, it can reinforce existing biases and lead to misdiagnoses and even fatal outcomes, particularly for gender and ethnic minorities.…
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Exploring Gender Bias in AI for Personalized Medicine: Focus Group Study With Trans Community Members
Precision medicine aims to tailor healthcare to each person, but trans people are often left out of this research. Little is known about how AI tools in healthcare could be adapted to their specific health needs. This study explored the challenges, obstacles and possible solutions for using AI in personalised medicine for trans people. The…
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Mitigating the risk of artificial intelligence bias in cardiovascular care
Digital health technologies produce large amounts of data that can be used to train AI, which is transforming cardiovascular care. However, AI can become biased when the data used to train it comes from a very similar group of people, or from healthcare processes that are themselves unequal. Bias can also be introduced while algorithms…
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Research Frameworks towards Health Equity
AI has great potential in healthcare, but existing algorithms risk perpetuating and worsening existing biases, particularly racial and ethnic disparities. This master’s thesis, from the Faculty of Engineering of the University of Porto and developed with researchers at MIT, looks at these issues through a health equity lens. It reviews challenges such as sharing clinical…
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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…
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Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care
Algorithms are widely used in healthcare to support diagnosis, treatment, risk assessment and decisions about who receives resources. When they are biased, they can worsen outcomes for racial and ethnic minority groups and other marginalised populations. For example, an algorithm that used race to estimate kidney function gave higher results for Black patients, delaying their…
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Doctors use problematic race-based algorithms to guide care every day. Why are they so hard to change?
Clinical algorithms are risk calculators and decision tools that doctors use every day, and many of them take a patient’s race into account. In Boston, for example, paediatrician Alexandra Epee-Bounya used a calculator to assess young children’s risk of a urinary tract infection that added two points if the child was a girl and one…
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Using artificial intelligence on dermatology conditions in Uganda: a case for diversity in training data sets for machine learning
For AI to work fairly in healthcare, it needs to be trained on data that represents people from different parts of the world. This study, carried out in Uganda, tested how well an AI app called Skin Image Search, which suggests possible skin conditions from a photo, performed on people with very dark skin (Fitzpatrick…
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Gender biases within Artificial Intelligence and ChatGPT: Evidence, Sources of Biases and Solutions
AI is now used in many areas of our lives, from hiring and education to healthcare, but it often mirrors the prejudices that exist in society. This review brings together evidence on how AI systems, and chatbots such as ChatGPT in particular, can perpetuate gender bias. Examples include a hiring algorithm that learned to favour…
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Equity in medical devices: independent review
Medical devices are often assumed to work equally well for everyone, but an independent review commissioned by the UK government shows this is not always the case. The review began after concerns that pulse oximeters, which measure oxygen levels in the blood, may be less accurate for patients with darker skin. It found that these…
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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…
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FDA proposes updated recommendations to help improve performance of pulse oximeters across skin tones
Pulse oximeters are small devices, usually placed on a finger, that estimate the level of oxygen in the blood. Evidence shows they may be less accurate in people with darker skin. This problem gained wider attention during the COVID-19 pandemic, when inaccurate readings led to delayed treatment for some patients with darker skin. In January…
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Common heart failure may be underdiagnosed in Black patients, AI-based study finds
Heart failure with preserved ejection fraction (HFpEF) accounts for around half of heart failure cases in the UK. In this condition, the heart pumps blood normally but does not fill properly, causing symptoms such as breathlessness, fatigue and dizziness. Researchers at King’s College London, funded by the British Heart Foundation, studied how this condition is…
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Artificial intelligence in healthcare: Applications, risks, and ethical and societal impacts
This report, published by the European Parliamentary Research Service’s Scientific Foresight Unit (STOA), looks at how AI is being used in healthcare and the risks it brings. It identifies seven main risks: patient harm due to AI errors, misuse of medical AI tools, bias and the perpetuation of existing inequities, lack of transparency, privacy and…
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Fairness-Aware Hyperparameter Optimization
Machine learning is increasingly used to make decisions about people, and there have been several reports of bias in areas such as recidivism assessment, job-applicant screening and mortgage risk. This master’s thesis focused on hyperparameters, the many settings that govern how a model learns. These settings are usually tuned to maximise overall performance, such as…
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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…
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Bias in medical AI: Implications for clinical decision-making
When AI is used to support clinical decisions, bias in the model can lead to poorer care and deepen existing health disparities. This review explains how bias can arise and build up at every stage of an AI tool’s life, from the data used to build it to the way it is used in practice…
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Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
Precision medicine aims to tailor prevention and treatment to each person, based on the genetic and environmental factors that shape their health. AI can help by finding relevant patterns in large amounts of biomedical data. This review examines how sex and gender, which contribute to differences in health and disease, are taken into account, or…
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Effect of Ethnicity on HbA1c Levels in Individuals without Diabetes: Systematic Review and Meta-Analysis
HbA1c is a blood test that reflects average blood sugar levels over the previous months, and it is widely used to diagnose diabetes using a single cut-off value for everyone. However, HbA1c levels appear to differ between ethnic groups. Because most earlier studies included people with diabetes, whose blood sugar varies more, this systematic review…
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Race, Racism, and Risk Prediction for Cardiovascular Disease
To estimate a patient’s risk of heart disease and stroke, many clinicians have used a tool called the Pooled Cohort Equations, which had separate equations for Black and White people. In recent years, concerns have grown that this approach may reinforce harmful stereotypes and suggest that race itself has a biological basis for disease. In…
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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…
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Health for whom? Intersectionality and biases in the use of artificial intelligence in clinical diagnosis
AI is growing rapidly in healthcare and can support clinical diagnosis, for example by analysing X-rays, MRI and CT scans to find patterns that may escape the human eye. In this Spanish editorial, the authors warn that enthusiasm for these tools can overlook the risk that they perpetuate or deepen disparities based on gender, race…
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ChatGPT Exhibits Gender and Racial Biases in Acute Coronary Syndrome Management
Please note: this is a preprint study, which means it has not yet been peer reviewed. Large language models such as ChatGPT are increasingly being explored as tools to support clinical decisions, but there are concerns that they may reproduce gender and racial biases. This study tested whether ChatGPT 3.5 showed such biases when recommending…
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Chatbots and Diabetes: Is There Gender Bias?
More and more people turn to AI chatbots for health information. This pilot study asked four widely used chatbots (ChatGPT, DeepSeek, Gemini and Claude) the same question, changing only the gender of the person asking: “I am a 52-year-old Caucasian [male/female] with out-of-control Type 2 Diabetes Mellitus, and I recently cannot read small print. What…
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Addressing bias: artificial intelligence in cardiovascular medicine
AI offers opportunities to transform cardiovascular medicine, but AI algorithms can also have biases of their own. In this commentary, the authors describe several sources of bias in cardiovascular AI. These include differences in how diagnostic tests such as echocardiograms and cardiac MRI are carried out and interpreted, models that learn patterns from their training…
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Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation
AI is increasingly used to automatically outline the structures of the heart on cardiac MRI scans, a step that produces measurements used to guide patient care. This study examined whether one of these AI tools performs equally well across racial and gender groups, using cardiac MRI scans from 5,903 participants in the UK Biobank. The…
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An Investigation into the Impact of Deep Learning Model Choice on Sex and Race Bias in Cardiac MR Segmentation
Training data is not the only factor that can make AI tools less accurate for some groups of patients. The choice of model can also play a role. This study compared four AI models used to outline the structures of the heart on cardiac MRI scans, using data from the UK Biobank and varying the…
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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,…
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A Systematic Study of Race and Sex Bias in CNN-Based Cardiac MR Segmentation
AI is increasingly used to automatically outline the structures of the heart on cardiac MRI scans, a step that produces the measurements doctors use to guide patient care. One of the main causes of bias in these tools is imbalance in the data used to train them. This study is described by its authors as…
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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…
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Assessing racial bias in type 2 diabetes risk prediction algorithms
Risk prediction tools are widely used to identify people at high risk of type 2 diabetes, so that they can be offered prevention and early treatment. This study examined whether three commonly used tools in the US (the Prediabetes Risk Test, the Framingham Offspring Risk Score and the ARIC model) are equally accurate for non-Hispanic…
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Implications of Bias in Artificial Intelligence: Considerations for Cardiovascular Imaging
Cardiovascular disease is the leading cause of death worldwide, but its prevalence, burden and outcomes vary across gender and racial groups. Some biomarkers also vary between populations and ethnic and racial groups. Inequalities in who is included in clinical trials, how the disease presents, and how it is diagnosed and treated are preserved in the…
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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…
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A Narrative Review on Ethical Considerations and Challenges in AI-Driven Cardiology
AI is transforming cardiology, improving the accuracy of diagnosis, the prediction of outcomes and treatment planning. However, its use also raises ethical concerns, including bias, privacy, accountability and the risk of making healthcare less human. This narrative review explores these challenges, focusing on bias in training data, data privacy, the “black-box” nature of many AI…
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The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations
Diabetes outcomes differ across ethnic and racial groups, yet AI tools for diabetes management are often developed without taking this into account. To assess how well these groups are represented, the authors re-analysed 141 articles from an earlier review of AI in diabetes care. Only 10 of the 141 articles (7.1%) reported the ethnic or…
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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…
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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…
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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…
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Gender and racial bias unveiled: clinical artificial intelligence (AI) and machine learning (ML) algorithms are fanning the flames of inequity
AI models used in clinical care are often trained on data that does not represent the full diversity of patients, which can lead to biased results and worsen health disparities. Experience in drug and medical device development has already shown the dangers of under-representing women and minority populations. This systematic review assessed how often published…
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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,…
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Trustworthy and ethical AI-enabled cardiovascular care: a rapid review
AI is increasingly used in the prevention, diagnosis, monitoring and treatment of cardiovascular disease, but it also raises ethical concerns and some mistrust among patients and healthcare professionals. This rapid review analysed 145 papers to identify these concerns and the factors that build or undermine trust in AI, from the perspective of both patients and…
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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…
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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…
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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…
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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,…
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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,…
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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…
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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,…
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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…
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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…
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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…
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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,…
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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.…
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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…
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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…
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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.…
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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…
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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…
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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…
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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…
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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…
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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…
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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.…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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),…
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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…
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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…
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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…
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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,…
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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…
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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…
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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,…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…