Addressing cognitive bias in medical language models
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland, College Park（马里兰大学帕克分校）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, Rama Chellappa
There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions. While promising, exam questions do not reflect the complexity of real patient-doctor interactions. In reality, physicians' decisions are shaped by many complex factors, such as patient compliance, personal experience, ethical beliefs, and cognitive bias. Taking a step toward understanding this, our hypothesis posits that when LLMs are confronted with clinical questions containing cognitive biases, they will yield significantly less accurate responses compared to the same questions presented without such biases. In this study, we developed BiasMedQA, a benchmark for evaluating cognitive biases in LLMs applied to medical tasks. Using BiasMedQA we evaluated six LLMs, namely GPT-4, Mixtral-8x70B, GPT-3.5, PaLM-2, Llama 2 70B-chat, and the medically specialized PMC Llama 13B. We tested these models on 1,273 questions from the US Medical Licensing Exam (USMLE) Steps 1, 2, and 3, modified to replicate common clinically-relevant cognitive biases. Our analysis revealed varying effects for biases on these LLMs, with GPT-4 standing out for its resilience to bias, in contrast to Llama 2 70B-chat and PMC Llama 13B, which were disproportionately affected by cognitive bias. Our findings highlight the critical need for bias mitigation in the development of medical LLMs, pointing towards safer and more reliable applications in healthcare.
Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving
Department of Electronic Engineering, Tsinghua University, Beijing, China（清华大学电子工程系）；College of AI, Tsinghua University, Beijing, China（清华大学人工智能学院）；School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China（北京邮电大学计算机科学学院）；iFLYTEK Research, Hefei, China（iFLYTEK研究院）
Emulating Human Cognitive Processes for Expert-Level Medical Question-Answering with Large Language Models
Dell Medical School at the University of Texas（德克萨斯大学戴尔医学院）；William Beaumont University Hospital（威廉·博蒙特大学医院）；Austin State Hospital（奥斯汀州立医院）；Institute of Mental Health, University of Nottingham（诺丁汉大学精神卫生研究所）；Government Medical College and Hospital, Chandigarh（昌迪加尔政府医学院及医院）；University of Augsburg（奥格斯堡大学）；McGovern Medical School and The University of Texas Health Science Center（麦戈文医学院与德克萨斯大学健康科学中心）；Sheikh Shakhbout Medical City（谢赫·沙赫布特医疗城）；Dr Akbar Niazi Teaching Hospital（阿克巴尔·尼亚兹博士教学医院）；Centro Médico ABC（ABC医疗中心）；Houston Regional Gastroenterology Institute（休斯顿地区胃肠病学研究所）；NeuroReef Labs（NeuroReef实验室）
Dissecting Role Cognition in Medical LLMs via Neuronal Ablation
School of Information, Renmin University of China（中国人民大学信息学院）；Center of Machine Learning Research, Peking University（北京大学机器学习研究中心）；School of Artificial Intelligence, Shanghai Jiaotong University（上海交通大学人工智能学院）；Health Informatics Committee of the China Society for Scientific and Technical Information, Renmin University of China（中国科学技术信息学会健康信息技术委员会，中国人民大学）；Institute for Advanced Algorithms Research (IAAR), Shanghai, China（先进算法研究所（IAAR），上海，中国）；Memtensor Research Center, Shanghai, China（Memtensor研究中心，上海，中国）
Analyzing Cognitive Differences Among Large Language Models through the Lens of Social Worldview
Wangxuan Institute of Computer Technology, Peking University（北京大学计算机技术研究院）；Information Management Department, Peking University（北京大学信息管理系）；Renmin University of China（中国人民大学）
Think Before You Comfort: Reflective Cognitive Alignment for Protocol-Grounded Elderly Stimulation Agents
The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Guangdong Provincial Laboratory of Artificial Intelligence and Digital Economy (Shenzhen)（广东省人工智能与数字经济实验室（深圳））
Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation
Sun Yat-sen University（中山大学）；Alibaba group（阿里巴巴集团）；Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology（广东省火灾科学与智能应急技术重点实验室）；Pazhou Lab（琶洲实验室）
AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière（索邦大学，巴黎脑研究所-ICM，法国国家科学研究中心，法国国家信息与自动化研究所，法国国家健康与医学研究院，巴黎公共医疗集团，皮蒂-萨尔皮特里埃医院）
Structured Outputs Enable General-Purpose LLMs to be Medical Experts
University of Bristol（布里斯托大学）；Lehigh University（利哈伊大学）；The University of Queensland（昆士兰大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of California, Merced（加利福尼亚大学默塞德分校）
Addressing Longstanding Challenges in Cognitive Science with Language Models
Center for Adaptive Rationality, Max Planck Institute for Human Development（适应性理性中心，马克斯·普朗克人类发展研究所）；Faculty of Psychology, University of Basel（心理学系，巴塞尔大学）
A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination
Biomedical Informatics and Medical Education（生物医学信息学与医学教育）；Health AI（健康人工智能）；Department of Information Sciences and Technology（信息科学与技术系）；Department of Linguistics（语言学系）
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing
University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard University（哈佛大学）；Rush University Medical Center（拉什大学医学中心）
MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation
Data Clinic, University Hospital of Nantes, France（南特大学医院数据诊所，法国）；Nantes Université, École Centrale Nantes, CNRS, LS2N, France（南特大学，中央理工学院南特分校，国家科学研究中心，LS2N，法国）
Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs
Technical University of Munich（慕尼黑工业大学）；TUM University Hospital Rechts der Isar（慕尼黑工业大学 Rechts der Isar 大学医院）；German Heart Center Munich（慕尼黑德国心脏中心）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；German Consortium for Translational Cancer Research（德国转化癌症研究联盟）
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering
Graduate School of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术研究院）；Faculty of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术学院）
