PT  - JOURNAL ARTICLE
AU  - Wang, Ting
AU  - Price, David W.
AU  - Bazemore, Andrew W.
TI  - Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence
AID  - 10.3122/jabfm.2024.240385R1
DP  - 2025 Aug 25
TA  - The Journal of the American Board of Family
                Medicine
4099  - http://www.jabfm.org/content/early/2025/08/22/jabfm.2024.240385R1.short
4100  - http://www.jabfm.org/content/early/2025/08/22/jabfm.2024.240385R1.full
AB  - Diagnostic errors are a significant challenge in health care, often resulting from gaps in physicians' knowledge and misalignment between confidence and diagnostic accuracy. Traditional educational methods have not sufficiently addressed these issues. This commentary explores how large language models (LLMs), a subset of artificial intelligence, can enhance diagnostic education by improving learning transfer and physicians' diagnostic accuracy. The American Board of Family Medicine (ABFM) is integrating LLMs into its Continuous Knowledge Self-Assessment (CKSA) platform to generate high-quality cloned diagnostic questions, implement effective spaced repetition strategies, and provide personalized feedback. By leveraging LLMs for efficient question generation and individualized learning, the initiative aims to transform continuous certification and lifelong learning, ultimately enhancing diagnostic accuracy and patient care.