Artificial Intelligence–Driven Personalised Surgical Curricula for Medical Students
Introduction Artificial Intelligence (AI) and immersive technologies such as Virtual Reality (VR) have the ability to personalise undergraduate surgical education by delivering adaptive feedback, standardised assessment, and resource-efficient training. Methods A focused review of 75 studies from a pool of >140 on the PubMed database explored AI-driven feedback, automated assessment, VR integration, and adaptive learning in undergraduate surgical training. Results Results across multiple trials demonstrate significant improvements in surgical training using AI-driven personalised approaches. These benefits were seen across the learning pathway - from initial skill acquisition to targeted feedback and final performance outcomes. A randomised pilot study reported high classification accuracies compared with human assessors for suturing (89%) and knot-tying (91%), lending to the credibility of AI-based performance assessments. Further trials involving participants without previous robotic surgery experience demonstrated significantly greater improvements in robotic suturing skills for those who received teaching videos based on AI-personalised feedback compared to those that did not (improvement 0.30 vs –0.02, p = 0.018). A Cochrane review involving a further 8 trials on AI-personalised surgical training showed similarly promising results, reporting reduced operative time by an average of 11.76 minutes (95% CI: 15.23 to 8.30 minutes), with adaptive AI feedback particularly beneficial for underperformers (p = 0.02). Conclusions AI-assisted feedback accurately identifies performance levels, whilst AI-personalised curricula and VR simulations show clear short-term improvements in novice surgical skill acquisition. These findings support the need for larger, multi-centre randomised controlled trials to assess long-term benefits. Integrating AI as a faculty-supervised adjunct offers a pragmatic and scalable path to transforming surgical education.
Authors
- Sui Ching Katherine Dai
- Marcus Owen Tsang
Institutions
- Newcastle University (GB)
Publication Details
- Journal
- Premier journal of science.
- Published
- 2025-12-27
- DOI
- https://doi.org/10.70389/pjs.100189
- Primary Topic
- Surgical Simulation and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00