AI in GME education

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Graduate Medical Education (GME)

Background:

• Artificial intelligence (AI) is increasingly adopted across graduate medical education (GME).
• AI applications span residency selection, clinical training, and postgraduate practice.
• Understanding AI's historical context and current integration in GME is essential.

Purpose of the Study:

• To review the history and current integration of AI in GME.
• To highlight the benefits, limitations, and challenges of AI in GME.
• To propose strategies for the ethical and effective implementation of AI in GME.

Main Methods:

• Literature review of AI applications in graduate medical education.
• Analysis of AI's impact on training efficiency, personalization, and workflow.
• Identification of ethical concerns, including bias, authenticity, and accountability.

Main Results:

• AI offers enhanced training through efficiency, personalized education, reduced clerical burden, workflow optimization, and diagnostic support.
• Significant challenges include algorithmic bias, threats to application authenticity, potential erosion of clinical reasoning, and unclear accountability.
• The integration of AI presents both opportunities for advancement and risks that require careful management.

Conclusions:

• A multidisciplinary committee is proposed to standardize AI use in GME.
• Recommendations include promoting ethical implementation, improving AI literacy, reducing bias, and ensuring equitable outcomes.
• Proactive measures are necessary to navigate the complexities of AI in medical training.