Artificial Intelligence in U.S. Surgical Training: A Scoping Review Mapping Current Applications and Identifying Gaps for Future Research Applications

Abstract

Artificial intelligence (AI) and machine learning (ML) are increasingly applied in general surgery education, yet their full impact on training across different learner levels remains unclear. The objectives of this study are to map the current use of AI/ML in general surgery education, with a focus on skill acquisition, risk stratification, and competency evaluation. The eligibility criteria included studies involving AI/ML interventions in general surgery training for medical students, residents, or practicing surgeons. Articles focused solely on clinical outcomes or non-surgical fields were excluded. A systematic search of databases, including PubMed, Scopus, Embase, and IEEE Xplore, was conducted. Data were extracted on study design, training level, type of AI/ML tool, educational focus, and key outcomes. A total of 18 studies were selected, which focused on simulation-based training, skill assessment, and decision support. Common barriers included a lack of standardization and limited integration into curricula. AI/ML shows promise in enhancing surgical education, but further research is needed to validate tools, measure impact, and address integration challenges.

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