Adjunct Clinical Assistant Professor of Preclinical Medicine Burrell College of Osteopathic Medicine Queen Creek, Arizona, United States
Chen F, Wang L, Hong J, Jiang J, Zhou L. Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models. J Am Med Inform Assoc. 2024;31(5):1172-1183. doi:10.1093/jamia/ocae060
He Xu, Yueqing Wang, Yangqin Xun, Ruitai Shao, Yang Jiao, Artificial intelligence for clinical reasoning: the reliability challenge and path to evidence-based practice, QJM: An International Journal of Medicine, 2025;, hcaf114, https://doi.org/10.1093/qjmed/hcaf114
Siontis GCM, Sweda R, Noseworthy PA, Friedman PA, Siontis KC, Patel CJ. Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials. BMJ Health Care Inform. 2021;28(1):e100466. doi:10.1136/bmjhci-2021-100466
Learning Objectives:
Identify the three foundational flaws—bias, generalizability, and opacity—that create a "cracked foundation" for medical AI.
Discuss how the legal concept of the "standard of care" is being disrupted, creating a "legal double bind" for clinicians who can be sued for both using and not using AI.
List and explain the key actions a frontline clinician should take to mitigate personal and professional risk, such as interrogating vendor data, documenting clinical dissent, and engaging with institutional leadership and liability insurers.
