Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence

Abstract

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.

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