Author: network

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

It finds that the model underperforms across several demographic subgroups, including gender, race, age, and socioeconomic status, often misclassifying individuals with depression as low-risk. For example, older adults and Black or low-income individuals were frequently ranked lower in risk than healthier younger or White individuals.

These biases stem from inconsistent relationships between sensed behaviors and depression across groups. The authors emphasized the need for subgroup-specific modeling to improve fairness and reliability in mental health AI tools.

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.