N. R. Srinivasa Raghavan, Prem C. Patel, David Limon, Miranda X. Morris, Jason W. Kempenich, Aashish Rajesh
In the rapidly advancing landscape of surgical education, the traditional apprenticeship model is being increasingly complemented by individualized learning, competency-based assessment, and data-driven feedback. Work-hour restrictions, administrative burdens, and limited operative exposure have intensified the need for innovative solutions to supplement faculty-led training. Artificial intelligence (AI) has emerged as a promising adjunct, offering scalable platforms for technical skill acquisition, personalized feedback, and structured progress tracking. Early applications include AI-guided simulation, feedback, natural language processing for resident evaluation, and advanced applicant-screening systems, which hold the potential to streamline holistic review while reducing faculty workload. Despite these advances, significant challenges remain, including bias mitigation, ethical data governance, and the need for rigorous outcome-based validation. The greatest promise lies in hybrid models, where AI augments rather than replaces mentorship, freeing faculty for complex, context-dependent teaching. With careful implementation, AI is poised to meaningfully transform surgical education worldwide.