The Impact of Large Language Models on Medical Education: Preparing for a Revolutionary Shift in Doctor Training

Abstract

Artificial intelligence holds immense potential to transform healthcare, though its widespread implementation has yet to be realized. This lag is partly because efforts have traditionally focused on easily predicted rather than easily actionable problems. Large language models (LLMs) represent a paradigm shift in our approach to artificial intelligence due to their accessibility and the fact that frontline clinicians are already testing them and identifying potential applications. LLMs in healthcare could significantly reduce clerical burdens, enhance patient education, and more. As we enter this new era of healthcare delivery, LLMs will bring both opportunities and challenges to medical education.^[1-5]

Future models should be designed to help trainees develop clinical reasoning skills, promote evidence-based medicine, and provide case-based training opportunities. LLMs may also necessitate changes in how clinical documentation is taught. Additionally, trainees can contribute to training and refining the next generation of LLMs as we explore the best ways to integrate these models into medical education.

Whether we are ready or not, LLMs will soon be integrated into various aspects of clinical practice. We must collaborate closely with students and educators to ensure these models are developed with trainees in mind, guiding medical education responsibly into the next era.^[21]