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

Area of Science:

• Medical Education
• Surgical Training
• Artificial Intelligence in Medicine

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly utilized in medical education.
• Their specific impact on general surgery training across various learner levels requires comprehensive understanding.

Purpose of the Study:

• To map the current applications of AI/ML in general surgery education.
• Focus on AI/ML's role in skill acquisition, risk stratification, and competency evaluation.
• Identify barriers and challenges to AI/ML integration in surgical curricula.

Main Methods:

• Systematic literature search across PubMed, Scopus, Embase, and IEEE Xplore.
• Inclusion criteria focused on AI/ML interventions in general surgery training for students, residents, and practicing surgeons.
• Data extraction on study design, training level, AI/ML tool type, educational focus, and outcomes.

Main Results:

• 18 studies were selected, highlighting AI/ML in simulation-based training, skill assessment, and decision support.
• Commonly identified barriers include lack of standardization and limited curriculum integration.
• AI/ML demonstrates potential for improving surgical education outcomes.

Conclusions:

• AI/ML tools show promise in enhancing general surgery education.
• Further research is essential to validate AI/ML tools and measure their impact.
• Addressing integration challenges is crucial for widespread adoption in surgical training.