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

Area of Science:

• Medical Education
• Artificial Intelligence in Healthcare
• Diagnostic Accuracy

Background:

• Diagnostic errors pose a significant healthcare challenge, stemming from knowledge gaps and confidence-accuracy misalignment.
• Traditional educational approaches have proven insufficient in addressing these diagnostic challenges.
• Physician diagnostic accuracy is critical for effective patient care.

Purpose of the Study:

• To explore the potential of large language models (LLMs) in enhancing diagnostic education.
• To improve learning transfer and physicians' diagnostic accuracy through AI-powered tools.
• To outline the integration of LLMs into continuous professional development platforms.

Main Methods:

• Utilizing large language models (LLMs) as a subset of artificial intelligence.
• Integrating LLMs into the American Board of Family Medicine's (ABFM) Continuous Knowledge Self-Assessment (CKSA) platform.
• Employing LLMs for generating high-quality diagnostic questions and personalized feedback.

Main Results:

• LLMs facilitate efficient generation of "cloned" diagnostic questions.
• Spaced repetition strategies can be effectively implemented using LLM-generated content.
• Personalized feedback is provided to enhance physician learning and diagnostic skills.

Conclusions:

• LLMs offer a transformative approach to continuous certification and lifelong learning in medicine.
• The integration of LLMs promises to enhance physician diagnostic accuracy.
• Ultimately, this initiative aims to improve patient care through advanced educational strategies.