mlsanityData to 7 Oct 2026

Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation

arXiv 2401.13867 ↗First posted 25 Jan 2024cs.CLcited by 31

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Abstract (arXiv)

Large 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. Through both qualitative and quantitative analyses, we 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 races, and disparities in treatment recommendations, etc. Our 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.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 25 Jan 2024.

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7 of 24 references.

References · 7

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,565
  3. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  4. 2023Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Healthcited by 118
  5. 2023Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicitycited by 86
  6. 2023Matching Patients to Clinical Trials with Large Language Modelscited by 68
  7. 2022PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systemscited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 19; 2026: 6.

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  1. 2024A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicinecited by 45
  2. 2025A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcarecited by 27
  3. 2025Multimodal Large Language Models for Medicine: A Comprehensive Surveycited by 16
  4. 2026FIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insightscited by 13
  5. 2024A survey on fairness of large language models in e-commerce: progress, application, and challengecited by 10
  6. 2025DAIQ: Auditing Demographic Attribute Inference from Question in LLMscited by 9
  7. 2025Elucidating Mechanisms of Demographic Bias in LLMs for Healthcarecited by 8
  8. 2024CLIMB: A Benchmark of Clinical Bias in Large Language Modelscited by 7
  9. 2025From Promising Capability to Pervasive Bias: Assessing Large Language Models for Emergency Department Triagecited by 5
  10. 2025Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMscited by 5
  11. 2025The World According to LLMs: How Geographic Origin Influences LLMs' Entity Deduction Capabilitiescited by 5
  12. 2025LLMs Do Not See Age: Assessing Demographic Bias in Automated Systematic Review Synthesiscited by 5
19 more
  1. 2024Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcarecited by 4
  2. 2024Entry-level guide to the use of large language models for medical researchcited by 3
  3. 2025No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Modelscited by 3
  4. 2025MEDEQUALQA: Evaluating Biases in LLMs with Counterfactual Reasoningcited by 3
  5. 2024Whose Journey Matters? Investigating Identity Biases in Large Language Models (LLMs) for Travel Planning Assistancecited by 2
  6. 2025On the Risk of Misleading Reports: Diagnosing Textual Biases in Multimodal Clinical AIcited by 2
  7. 2025Dropouts in Confidence: Moral Uncertainty in Human-LLM Alignmentcited by 2
  8. 2026CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomographycited by 2
  9. 2025OrthoInsight: Rib Fracture Diagnosis and Report Generation Based on Multi-Modal Large Modelscited by 1
  10. 2025Toward Revealing Nuanced Biases in Medical LLMscited by 1
  11. 2025Towards Experience-Centered AI: A Framework for Integrating Lived Experience in Design and Developmentcited by 1
  12. 2025Can SAEs reveal and mitigate racial biases of LLMs in healthcare?cited by 1
  13. 2025HIVMedQA: Benchmarking large language models for HIV medical decision supportcited by 0
  14. 2025A Systematic Review on the Generative AI Applications in Human Medical Genomicscited by 0
  15. 2025Strategic Innovation Management in the Age of Large Language Models Market Intelligence, Adaptive R&D, and Ethical Governancecited by 0
  16. 2026Magic, Madness, Heaven, Sin: LLM Output Diversity is Everything, Everywhere, All at Oncecited by 0
  17. 2026First, Do No Harm (With LLMs): Mitigating Racial Bias via Agentic Workflowscited by 0
  18. 2026Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainabilitycited by 0
  19. 2026Same Facts, Different Updates: Inference Setup Shapes LLM Behavior in Medical Allocationcited by 0