Usability, Acceptability, and Implementation of Artificial Intelligence (AI) and Machine Learning (ML) Techniques in Surgical Coaching and Training: A Scoping Review

Abstract

Objective: To define the current state of peer-reviewed literature demonstrating the usability, acceptability, and implementation of artificial intelligence (AI) and machine learning (ML) techniques in surgical coaching and training.

Design: We conducted a literature search with defined inclusion and exclusion criteria. We searched five scholarly databases: MEDLINE via PubMed, Embase via Elsevier, Scopus via Elsevier, Cochrane Central Register of Controlled Trials, and the Healthcare Administration Database via ProQuest. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines.

Results: Only 4 articles met the inclusion criteria and used standardized methods for performance evaluation with expert observation. We found no literature examining the impact on performance, user acceptance, or implementation of AI/ML techniques used for surgical coaching and training. We highlight the need for qualitative and quantitative research demonstrating these techniques' effectiveness before broad implementation.

Conclusion and relevance: We emphasize the need for research to specifically evaluate performance, impact, user acceptance, and implementation of AI/ML techniques. Incorporating these facets of research when developing AI/ML techniques for surgical training is crucial to ensure emerging technology meets user needs without increasing cognitive burden or frustrating users.

Keywords: artificial intelligence; competency-based education; machine learning; usability; user-centered design.