The Role of Augmented Reality in Surgical Training: A Systematic Review
Abstract
This review aims to provide an update on the role of augmented reality (AR) in surgical training and investigate whether the use of AR improves performance measures compared to traditional approaches in surgical trainees. PUBMED, EMBASE, Google Scholar, Cochrane Library, British Library and Science Direct were searched following PRIMSA guidelines. All English language original studies pertaining to AR in surgical training were eligible for inclusion. Qualitative analysis was performed and results were categorised according to simulator models, subsequently being evaluated using Messick's framework for validity and McGaghie's translational outcomes for simulation-based learning. Of the 1132 results retrieved, 45 were included in the study. 29 platforms were identified, with the highest 'level of effectiveness' recorded as 3. In terms of validity parameters, 10 AR models received a strong 'content validity' score of 2.15 models had a 'response processes' score ≥ 1. 'Internal structure' and 'consequences' were largely not discussed. 'Relations to other variables' was the best assessed criterion, with 9 platforms achieving a high score of 2. Overall, the Microsoft HoloLens received the highest level of recommendation for both validity and level of effectiveness. Augmented reality in surgical education is feasible and effective as an adjunct to traditional training. The Microsoft HoloLens has shown the most promising results across all parameters and produced improved performance…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.