Large Language Models and Medical Education: Preparing for a Rapid Transformation in How Trainees Will Learn to Be...

Area of Science:

• Healthcare Technology
• Artificial Intelligence in Medicine
• Medical Education

Background:

• Artificial intelligence (AI) holds significant promise for revolutionizing healthcare, yet widespread implementation remains limited.
• Current AI applications often focus on predictable rather than actionable healthcare problems.
• Large language models (LLMs) represent a significant advancement, offering accessibility and rapid testing by clinicians.

Purpose of the Study:

• To explore the potential of LLMs in healthcare delivery and medical education.
• To identify opportunities and challenges associated with LLM integration in clinical practice and training.
• To emphasize the need for developing LLMs that support clinical reasoning, evidence-based medicine, and case-based training.

Main Methods:

• Analysis of current trends and potential applications of LLMs in healthcare.
• Discussion of the impact of LLMs on medical education, including clinical documentation and trainee skill development.
• Exploration of the collaborative role of trainees in developing and refining future LLM applications.

Main Results:

• LLMs have the potential to significantly reduce clerical workload for healthcare professionals.
• LLMs can improve patient education by bridging information gaps.
• LLMs are poised to alter medical curricula, emphasizing clinical reasoning and evidence-based practices.

Conclusions:

• LLMs are rapidly becoming integrated into clinical practice and medical education.
• Future LLM development must prioritize support for trainees and educators.
• Collaborative efforts between developers, educators, and trainees are essential for the responsible integration of LLMs in healthcare.