Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions

Abirami Kirubarajan; Dylan Young; Shawn Khan; Noelle Crasto; Mara Sobel; Dafna Sussman

doi:10.1016/j.jsurg.2021.09.012

Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions

J Surg Educ. 2022 Mar-Apr;79(2):500-515. doi: 10.1016/j.jsurg.2021.09.012. Epub 2021 Oct 30.

Authors

Abirami Kirubarajan¹, Dylan Young², Shawn Khan³, Noelle Crasto², Mara Sobel⁴, Dafna Sussman⁵

Affiliations

¹ Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. Electronic address: abi.kirubarajan@mail.utoronto.ca.
² Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada.
³ Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
⁴ Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Ryerson University and St. Michael's Hospital, Toronto, Ontario, Canada.
⁵ Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Ryerson University and St. Michael's Hospital, Toronto, Ontario, Canada; Department of Obstetrics and Gynaecology, University of Toronto, Toronto, Ontario, Canada; The Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, Ontario, Canada.

PMID: 34756807
DOI: 10.1016/j.jsurg.2021.09.012

Abstract

Objective: To synthesize peer-reviewed evidence related to the use of artificial intelligence (AI) in surgical education DESIGN: We conducted and reported a scoping review according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis with extension for Scoping Reviews guideline and the fourth edition of the Joanna Briggs Institute Reviewer's Manual. We systematically searched eight interdisciplinary databases including MEDLINE-Ovid, ERIC, EMBASE, CINAHL, Web of Science: Core Collection, Compendex, Scopus, and IEEE Xplore. Databases were searched from inception until the date of search on April 13, 2021.

Setting/participants: We only examined original, peer-reviewed interventional studies that self-described as AI interventions, focused on medical education, and were relevant to surgical trainees (defined as medical or dental students, postgraduate residents, or surgical fellows) within the title and abstract (see Table 2). Animal, cadaveric, and in vivo studies were not eligible for inclusion.

Results: After systematically searching eight databases and 4255 citations, our scoping review identified 49 studies relevant to artificial intelligence in surgical education. We found diverse interventions related to the evaluation of surgical competency, personalization of surgical education, and improvement of surgical education materials across surgical specialties. Many studies used existing surgical education materials, such as the Objective Structured Assessment of Technical Skills framework or the JHU-ISI Gesture and Skill Assessment Working Set database. Though most studies did not provide outcomes related to the implementation in medical schools (such as cost-effective analyses or trainee feedback), there are numerous promising interventions. In particular, many studies noted high accuracy in the objective characterization of surgical skill sets. These interventions could be further used to identify at-risk surgical trainees or evaluate teaching methods.

Conclusions: There are promising applications for AI in surgical education, particularly for the assessment of surgical competencies, though further evidence is needed regarding implementation and applicability.

Keywords: Artificial intelligence; machine learning; surgery; surgical education; technology.

Publication types

Research Support, Non-U.S. Gov't
Systematic Review

MeSH terms

Artificial Intelligence*