ArticleCommunications medicine2024
Unmasking and quantifying racial bias of large language models in medical report generation.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Large language models in healthcare: applications, evaluation frameworks, and governance pathways - a scoping review and multidimensional framework.Frontiers in digital health · 2026Pooled it
- Evaluating and addressing demographic disparities in medical large language models: a systematic review.International journal for equity in health · 2025Pooled it
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- Identifying suicide-related language in smartphone keyboard entries among high-risk adolescents.NPJ digital medicine · 2026Article
- From Promising Capabilities to Pervasive Bias: Assessing Large Language Models for Emergency Department Triage.Journal of healthcare informatics research · 2026Article
- Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 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
- Development and prospective shadow evaluation of a domain-specific large language model for emergency neurological diagnosis.NPJ digital medicine · 2026Article
- Clinical Use of Non-Certified Generative AI in Healthcare: Governing the Regulatory Grey Zone from Convenience to Legal Accountability.Journal of medical systems · 2026Article
- Obedience to Unsafe Clinical Instructions: How Large Language Models Respond to Authority Cues.Research square · 2026Article
- Large Language Models Using Clinical Text in Pediatrics: A Scoping Review.JAMA network open · 2026Article
- Benchmark evaluation of large language models for clinical decision support in headache management.Journal of oral & facial pain and headache · 2026Article
- Assessment of ChatGPT-5 as an Artificial Intelligence Tool for Exploring Emerging Dimensions of Clinical Simulation: A Proof-of-concept Study.Journal of medical systems · 2026Article
- Detecting stigmatizing language in clinical notes with large language models for addiction care.npj health systems · 2026Article
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
- LLMs Can Do Medical Harm: Stress-Testing Clinical Decisions Under Social Pressure.medRxiv : the preprint server for health sciences · 2025Article
- Sociodemographic Bias in Large Language Model-Assisted Gastroenterology.JAMA network open · 2025Article
- Harm Reduction Strategies for Thoughtful Use of Large Language Models in the Medical Domain: Perspectives for Patients and Clinicians.Journal of medical Internet research · 2025Article
Corrections and comments
- Update of
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting their utility in medical applications. Despite few attempts in the past, the precise impact and extent of these biases remain uncertain.
methodsWe use LLMs to generate responses that predict hospitalization, cost and mortality based on real patient cases. We manually examine the generated responses to identify biases.
resultsWe find that these models tend to project higher costs and longer hospitalizations for white populations and exhibit optimistic views in challenging medical scenarios with much higher survival rates. These biases, which mirror real-world healthcare disparities, are evident in the generation of patient backgrounds, the association of specific diseases with certain racial and ethnic groups, and disparities in treatment recommendations, etc.
conclusionsOur findings underscore the critical need for future research to address and mitigate biases in language models, especially in critical healthcare applications, to ensure fair and accurate outcomes for all patients.
Identifiers
What OpenQuestion holds
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.