Artificial Intelligence in Surgical Training and Applications to Otolaryngology: A Scoping Review

Abstract

OBJECTIVE: Traditional evaluations of surgical skills in otolaryngology rely heavily on subjective assessments, which are prone to variability and bias. This study aims to examine advancements in artificial intelligence (AI) applications for surgical skills evaluation with a focus on their potential to enhance otolaryngology education. DATA SOURCES: A systematic search of MEDLINE, Embase, Cochrane Database of Systematic Reviews, and Google Scholar was conducted using search terms related to AI and surgical skills evaluation. REVIEW METHODS: A structured review of the literature up to November 2024 was performed. Two independent reviewers identified and analyzed relevant studies. Reference lists of selected articles were also screened to ensure comprehensiveness. RESULTS: A total of 34 studies met inclusion criteria. Of these, 56% (19/34) evaluated basic surgical tasks, such as hand-tying, open suturing, and robotic or laparoscopic procedures using bench-top simulators, while 44% (15/34) focused on specific surgical procedures across specialties, including otolaryngology (mastoidectomy, septoplasty, endoscopic sinus surgery, endoscopic carotid injury management), neurosurgery, urology, and general surgery. AI methods applied included deep learning, machine learning, and computer vision techniques. Classification accuracy ranged from 66% to 100% for kinematic, motion, and force data, and from 60% to 96% for video-based analyses of surgical skills. CONCLUSION: AI-driven assessment tools hold significant promise for otolaryngology surgical education. Automated feedback mechanisms can provide trainees with objective, data-driven insights into their performance, enabling enhanced benchmarking and accelerating learning curves. By adopting AI, otolaryngology has the potential to advance its training methodologies and improve outcomes for both trainees and patients. LEVEL OF EVIDENCE: N/A.

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