ArticleNPJ digital medicine2025
Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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Who cites it
25 citing papers in PubMed.
- Article
- Human Touch vs. Tech Talk: Mental Health Clinicians' Perspectives on Artificial Intelligence in Systemic Practice.Journal of marital and family therapy · 2026Article
- Assessing and mitigating demographic bias in large language models for diagnostic radiology.Japanese journal of radiology · 2026Article
- Implicit Bias in Large Language Model Diagnosis of Eating Disorders: Experimental Vignette Study.JMIR AI · 2026Article
- GPT-4 improves sex-specificity in cardiovascular patient education but may perpetuate gender biases: A mixed-methods audit.PLOS digital health · 2026Article
- Deployment protocol and base model jointly determine residual risk in rule-based clinical LLM supervision.Research square · 2026Article
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Artificial intelligence needs better health systems to reduce inequalities.NPJ digital medicine · 2026Review
- Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026Article
- When silence is safer: a review and decision-theoretic framework for LLM abstention in healthcare.NPJ digital medicine · 2026Article
- Implicit Gender, Racial, and Ethnic Biases in Large Language Models: An Audit Study of Automated Psychiatric Diagnoses.Mayo Clinic proceedings. Digital health · 2026Article
- Demographic prompt cues shift clinical recommendations in multimodal large language models: a multi-model audit of 25,380 dental radiograph assessments.BMC oral health · 2026Article
- Acceptability of Using Large Language Models to Support Smoking Cessation Attempts: a Qualitative Study Among African Americans Who Smoke.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026Article
- iS2C2: a cointelligent platform for mechanistic discovery of disease cellular crosstalk.Signal transduction and targeted therapy · 2026Article
- Digital Mental Health Through an Intersectional Lens: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Evaluating the Efficacy of AI-Based Interactive Assessments Using Large Language Models for Depression Screening: Development and Usability Study.JMIR formative research · 2026Article
- Article
- Integrated approaches to geriatric mental disorders: pathophysiology, interventions, and precision medicine horizons.Frontiers in psychiatry · 2026Review
- Artificial intelligence in psychiatry: clinical applications, limitations, and ethical challenges.Frontiers in behavioral neuroscience · 2026Article
- Profile-associated financial and access-related framing in LLM-generated pediatric asthma referral plans: a factorial audit of seven large language models.Frontiers in digital health · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI), particularly large language models (LLMs), is increasingly integrated into mental health care. This study examined racial bias in psychiatric diagnosis and treatment across four leading LLMs: Claude, ChatGPT, Gemini, and NewMes-15 (a local, medical-focused LLaMA 3 variant). Ten psychiatric patient cases representing five diagnoses were presented to these models under three conditions: race-neutral, race-implied, and race-explicitly stated (i.e., stating patient is African American). The models' diagnostic recommendations and treatment plans were qualitatively evaluated by a clinical psychologist and a social psychologist, who scored 120 outputs for bias by comparing responses generated under race-neutral, race-implied, and race-explicit conditions. Results indicated that LLMs often proposed inferior treatments when patient race was explicitly or implicitly indicated, though diagnostic decisions demonstrated minimal bias. NewMes-15 exhibited the highest degree of racial bias, while Gemini showed the least. These findings underscore critical concerns about the potential for AI to perpetuate racial disparities in mental healthcare, emphasizing the necessity of rigorous bias assessment in algorithmic medical decision support systems.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.