This study evaluates whether large language models (LLMs) exhibit biases towards medical professionals. Fictitious candidate resumes were created to control for identity factors while maintaining consistent qualifications. Three LLMs (GPT-4, Claude-3-haiku, and Mistral-Large) were tested using a standardized prompt to evaluate resumes for specific residency programs. Explicit bias was tested by changing gender and race information, while implicit bias was tested by changing names while hiding race and gender. Physician data from the Association of American Medical Colleges was used to compare with real-world demographics. 900,000 resumes were evaluated. All LLMs exhibited significant gender and racial biases across medical specialties. Gender preferences varied, favoring male candidates in surgery and orthopedics, while preferring females in dermatology, family medicine, obstetrics and gynecology, pediatrics, and psychiatry. Claude-3 and Mistral-Large generally favored Asian candidates, while GPT-4 preferred Black and Hispanic candidates in several specialties. Tests revealed strong preferences towards Hispanic females and Asian males in various specialties. Compared to real-world data, LLMs consistently chose higher proportions of female and underrepresented racial candidates than their actual representation in the medical workforce. GPT-4, Claude-3, and Mistral-Large showed significant gender and racial biases when evaluating medical professionals for residency selection. These findings highlight the potential for LLMs to perpetuate biases and compromise healthcare workforce diversity if used without proper bias mitigation strategies.
Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
Mayo Clinic（梅奥诊所）；the First Affiliated Hospital of Nanchang University（南昌大学第一附属医院）；University of Georgia（佐治亚大学）；University of Electronic Science and Technology of China（电子科技大学）；Eye Institute of Xiamen University（厦门大学眼科研究所）
FairMedQA: Benchmarking Bias in Large Language Models for Medical Question Answering
King’s College London（伦敦国王学院）；University of Electronic Science and Technology of China（电子科技大学）；Sun Yat-sen University（中山大学）；York University（约克大学）；Southern University of Science and Technology（南方科技大学）；University College London（伦敦大学学院）；Tsinghua University（清华大学）
Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification
Centre for Intelligent Machines, McGill University, Montreal, Canada（智能机器中心，麦吉尔大学，加拿大）；Mila -- Quebec AI Institute, Montreal, Canada（魁北克人工智能研究所）；Stanford University, Stanford, USA（斯坦福大学）；University of Copenhagen, Copenhagen, Denmark（哥本哈根大学）
Dissecting Medical Referral Mechanisms in Health Services: Role of Physician Professional Networks
Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；Nova School of Business and Economics, Universidade Nova de Lisboa（新里斯本大学诺瓦商业与经济学院）；Nova School of Science and Technology, Universidade Nova de Lisboa（新里斯本大学诺瓦科学与技术学院）
Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals
University of Edinburgh（爱丁堡大学）；Inria Bordeaux（波尔多信息与自动化研究院）
本文重新构建 ML 偏见问题，通过创建模型识别英语数据中的偏见而非消除它，并与信息与遗产专业人员合作评估，揭示了 ML 识别偏见的局限性，提出扩展 ML 公平性方法的混合研究路径。
Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging
Massachusetts Institute of Technology（麻省理工学院）；University of California, San Diego（加州大学圣地亚哥分校）；University of Washington（华盛顿大学）；University of Washington School of Medicine（华盛顿大学医学院）
Fairness via AI: Bias Reduction in Medical Information
University of Connecticut（康涅狄格大学）；AuCoDe；Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Meedan；University of Washington School of Medicine（华盛顿大学医学院）；Michigan State University College of Human Medicine（密歇根州立大学人类医学院）
Detecting algorithmic bias in medical-AI models using trees
School of Industrial and Systems Engineering, Georgia Institute of Technology（佐治亚理工学院工业与系统工程学院）；Department of Biomedical Informatics, Emory University School of Medicine（埃默里大学医学院生物医学信息学系）；Department of Surgery, Duke University School of Medicine（杜克大学医学院外科系）
