This paper is in the following e-collection/theme issue:

Generative Language Models Including ChatGPT (708) Artificial Intelligence (2129) Clinical Information and Decision Making (1915) Decision Support for Health Professionals (1539) Research Instruments, Questionnaires, and Tools (738) Machine Learning (2076) Simulation (164) Data Science (357) Chatbots and Conversational Agents (740) Validity, Reliability, and Bias (110) Theoretical Frameworks and Concepts (338) Case Report (10)

Published on in Vol 26 (2024)

This is a member publication of National University of Singapore

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59439, first published .
Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study

Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study

Mitigating Cognitive Biases in Clinical Decision-Making Through Multi-Agent Conversations Using Large Language Models: Simulation Study

Authors of this article:

Yuhe Ke1, 2 Author Orcid Image ;   Rui Yang1 Author Orcid Image ;   Sui An Lie2 Author Orcid Image ;   Taylor Xin Yi Lim2 Author Orcid Image ;   Yilin Ning1 Author Orcid Image ;   Irene Li3 Author Orcid Image ;   Hairil Rizal Abdullah2 Author Orcid Image ;   Daniel Shu Wei Ting1, 4 Author Orcid Image ;   Nan Liu1, 5 Author Orcid Image
Article Authors Cited by (21) Tweetations (33) Metrics

Journals

  1. Bousquet C, Beltramin D. Advantages and Inconveniences of a Multi-Agent Large Language Model System to Mitigate Cognitive Biases in Diagnostic Challenges. Journal of Medical Internet Research 2025;27:e69742 View
  2. Yang R, Ning Y, Keppo E, Liu M, Hong C, Bitterman D, Ong J, Ting D, Liu N. Retrieval-augmented generation for generative artificial intelligence in health care. npj Health Systems 2025;2(1) View
  3. Kim S, Schramm S, Adams L, Braren R, Bressem K, Keicher M, Platzek P, Paprottka K, Zimmer C, Hedderich D, Wiestler B. Benchmarking the diagnostic performance of open source LLMs in 1933 Eurorad case reports. npj Digital Medicine 2025;8(1) View
  4. Su H, Sun Y, Li R, Zhang A, Yang Y, Xiao F, Duan Z, Chen J, Hu Q, Yang T, Xu B, Zhang Q, Zhao J, Li Y, Li H. Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis. Journal of Medical Internet Research 2025;27:e72062 View
  5. Gorenshtein A, Sorka M, Khateb M, Aran D, Shelly S. Agent-guided AI-powered interpretation and reporting of nerve conduction studies and EMG (INSPIRE). Clinical Neurophysiology 2025;177:2110792 View
  6. Yao Z, Duan L, Xu S, Chi L, Sheng D. Performance of Large Language Models in the Non-English Context: Qualitative Study of Models Trained on Different Languages in Chinese Medical Examinations. JMIR Medical Informatics 2025;13:e69485 View
  7. Mahajan A, Obermeyer Z, Daneshjou R, Lester J, Powell D. Cognitive bias in clinical large language models. npj Digital Medicine 2025;8(1) View
  8. Ke Y, Leong Y, Jin L, Elangovan K, Abdullah H, Sia A, Ong J, Shah N, Wong T, Wei Ting D. Differential reasoning and chain-of-thought processes in Deepseek-R1 and Open AI o3-mini-high for determining American Society of Anesthesiologists physical status. British Journal of Anaesthesia 2025;135(3):800 View
  9. Iacobucci G, Leo R, Pristach C. Examining the influence of cognitive bias delaying the diagnosis and treatment of Anti-NMDA receptor encephalitis. Personalized Medicine in Psychiatry 2025;51-52:100164 View
  10. Wu X, Huang Y, He Q. Diagnostic performance of newly developed large language models in critical illness cases: A comparative study. International Journal of Medical Informatics 2025;204:106088 View
  11. Chen X, Chen R, Xu P, Wan X, Zhang W, Yan B, Shang X, He M, Shi D. From visual question answering to intelligent AI agents in ophthalmology. British Journal of Ophthalmology 2025:bjo-2024-326097 View
  12. Jin L, Ong J, Elangovan K, Ke Y, Pyle A, Ting D, Liu N. Large Language Models in Randomized Controlled Trials Design: Observational Study. Journal of Medical Internet Research 2025;27:e67469 View
  13. Wiest I, Bhat M, Clusmann J, Schneider C, Jiang X, Kather J. Large language models for clinical decision support in gastroenterology and hepatology. Nature Reviews Gastroenterology & Hepatology 2025;22(11):773 View
  14. Li R, Wang X, Berlowitz D, Mez J, Lin H, Yu H. CARE-AD: a multi-agent large language model framework for Alzheimer’s disease prediction using longitudinal clinical notes. npj Digital Medicine 2025;8(1) View
  15. Chow M, Ng O. Beyond Chatbots: Moving Toward Multistep Modular AI Agents in Medical Education. JMIR Medical Education 2025;11:e76661 View
  16. Theodorakopoulos L, Theodoropoulou A, Halkiopoulos C. Cognitive Bias Mitigation in Executive Decision-Making: A Data-Driven Approach Integrating Big Data Analytics, AI, and Explainable Systems. Electronics 2025;14(19):3930 View
  17. Wang Y, Tan W, Cheng S, Peng C, Peng J, Qin F, Tang L, Zhu T, Wu B, Liu J, Wang J. Large Language Model Agent for Managing Patients With Suspected Hypertension. Hypertension 2025 View
  18. Zheng X, Zou H, Wu L, Dong P, Yuan W, Chen Y. Large language model-driven agents in nursing practice: A scoping review. International Journal of Nursing Sciences 2025;12(6):532 View
  19. Xu L, Cong X, Wang R, Li N, Liu X, Wang R, Xu C. Performance of the Large Language Models on the Chinese National Nurse Licensure Examination: Cross-Sectional Evaluation Study. JMIR Medical Informatics 2025;13:e78279 View
  20. Yang T, Xiao Y, Bao Z, Hao J, Peng J. The rise and potential opportunities of large language model agents in bioinformatics and biomedicine. Briefings in Bioinformatics 2025;26(6) View

Books/Policy Documents

  1. Kalathas D, Menychtas A, Maglogiannis I, Tsanakas P. Artificial Intelligence Applications and Innovations. View