Background: Operative surgery is a core discipline in medical education that requires mastery of anatomical knowledge, psychomotor skills, and intraoperative decision-making. Traditional teaching approaches often struggle to provide sufficient practice opportunities and personalised feedback for large student groups.Objective: This study aims to identify and summarise effective methods for using artificial intelligence (AI) in teaching operative surgery in higher medical education institutions.Methods: A narrative review of contemporary educational practices and technological innovations was conducted. AI-based tools were classified into practical categories relevant to operative surgery training.
Results: Five groups of effective AI applications were identified: AI-enhanced simulation, computer-vision–based skills assessment, intelligent tutoring systems, adaptive e-learning platforms, and mobile AI tools. These methods improve student engagement, enhance the accuracy of skill assessment, and expand opportunities for independent practice.Conclusion: AI offers promising opportunities to modernise operative surgery education. When integrated into a competency-based curriculum, AI can strengthen student performance, provide personalised training pathways, and support safe skill acquisition.

Artificial Intelligence; Operative Surgery; Surgical Education; Simulation-Based Training; Virtual Reality; Augmented Reality; Intelligent Tutoring Systems; Adaptive Learning; Computer Vision; Medical Education; Technical Skill Assessment; Digital Pedagogy.

Zhu, E., Hadadgar, A., Masiello, I., & Zary, N. (2014). Augmented reality in healthcare education: An integrative review. PeerJ, 2, e469.

Panait, L., Akkary, E., Bell, R. L., Roberts, K. E., & Duffy, A. J. (2010). The role of simulation in surgical training. International Journal of Surgery, 8(2), 105–112.

Maier-Hein, L., Vedula, S. S., Speidel, S., et al. (2017). Surgical data science: Enabling next-generation surgery. Nature Biomedical Engineering, 1(9), 691–696.

Issenberg, S. B., McGaghie, W. C., Petrusa, E. R., Gordon, D. L., & Scalese, R. J. (2005). Features and uses of high-fidelity medical simulations that lead to effective learning. Medical Teacher, 27(1), 10–28.

Aïm, F., Lonjon, G., Hannouche, D., & Nizard, R. (2016). Effects of virtual reality training on surgical performance: A systematic review. Journal of Surgical Education, 73(4), 770–779.

Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.

(Operativ jarrohlikdagi computer vision modellarining asosiy tamoyillariga zamin yaratgan.)

Seymour, N. E., Gallagher, A. G., Roman, S. A., et al. (2002). Virtual reality training improves operating room performance: Results of a randomized, double-blinded study. Annals of Surgery, 236(4), 458–464.

Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018). Artificial intelligence in surgery: Promises and perils. Annals of Surgery, 268(1), 70–76.

Singh, P., Aggarwal, R., Tahir, M., et al. (2015). A randomized controlled study to evaluate the role of video-based coaching in laparoscopic training. Surgical Endoscopy, 29(4), 1159–1166.

Jiang, T., Zhang, Q., Zhang, Y., et al. (2021). Artificial intelligence in surgical education: A new paradigm. Frontiers in Surgery, 8, 666208.

Lindeman, B., Ward, M., Carter, S., et al. (2021). Generative artificial intelligence for surgical education: Opportunities and challenges. Journal of Surgical Education, 78(6), 2099–2107.