Uncovering the Role of AI in Surgical Residency Training Programs

Abstract

Abstract 

Introduction: 

After completing medical school, to become a board-certified surgeon in the United States, trainees must complete a 5 to 7 year period of training known as residency. This time focuses on subspecialty rotations, operative responsibility, and competency evaluation. Traditionally dependent on direct supervision and hands-on experience, with the rise of artificial intelligence (AI), training is changing, leading to discussions about its role in medical education. The DECODE framework provides guidance for integrating digital skills and ensuring AI supports technical abilities while also promoting professionalism, ethics, and patient centered care. This paper explores how AI impacts surgical residency training. 

Methods: 

A systematic review was conducted using PubMed (MEDLINE) with the search terms “surgery residency training AND artificial intelligence.” Studies published in English between January 2020 and January 2025 were screened according to modified PRISMA guidelines. Twelve studies met the inclusion criteria and covered various surgical specialties and international programs.

Results: 

Four main themes emerged: accuracy, efficiency, skill development, and training efficiency. AI-assisted platforms improved precision in simulations, standardized assessments, reduced faculty workload, and created adaptive learning paths. These features shortened learning curves, improved cognitive and technical skills, and boosted residents' confidence.

Conclusion:  

AI has the capacity to change surgical education by standardizing training, expanding access, and enhancing outcomes. By aligning with DECODE, AI’s role in residency programs shows how technology can strengthen the foundations of medical education, which has implications for other areas of healthcare.