Artificial Intelligence–Based Approaches for the Assessment of Surgical Skills: A Systematic Review
DOI:
https://doi.org/10.63053/ijhes.183Keywords:
Artificial Intelligence, Surgical Skills Assessment, Machine Learning, Deep Learning, Surgical Education, Simulation-Based Training, Surgical Performance Evaluation, Computer-Assisted Surgery, Surgical Decision Support, Operative CompetencyAbstract
Background: In recent years, artificial intelligence (AI) has emerged as an innovative tool for surgical training and skill assessment. The expansion of simulation-based methods and robotic technologies has enabled the collection of motion, video, and multimodal data, which can be leveraged by AI models to analyze and classify surgeons’ skills. However, the existing evidence is scattered and heterogeneous, highlighting the need for a systematic review to evaluate applications, benefits, limitations, and future perspectives. This study aims to provide a systematic review of AI applications in surgical skill assessment, analyze algorithm performance, examine educational feedback, and identify current limitations and future research opportunities in this field.
Methods: A systematic search was conducted across major scientific databases, including PubMed, Scopus, Web of Science, Springer, Nature, JAMA Network, and ScienceDirect. Studies published between 2018 and 2026 that focused on AI applications in surgical skill assessment were included. Data were extracted regarding input data type, algorithm type, performance accuracy, feedback applications, and model validation. Both qualitative and quantitative analyses were performed.
Results: Based on 60 identified studies, AI models demonstrated the capability to analyze motion, video, and multimodal data, achieving surgeon skill classification accuracies ranging from 80% to 95%. The use of personalized and real-time feedback enhanced skill acquisition and facilitated transfer learning. Algorithms were also applied for surgical phase recognition, risk prediction, and clinical decision support. Nevertheless, limitations such as restricted multicenter validation, lack of standardized data collection protocols, and low interpretability of certain models were reported.
Conclusion: AI has the potential to play an effective role in surgical education, skill assessment, and clinical decision support, complementing human instructors in the operating room. To fully exploit AI’s potential, the development of standardized frameworks, multicenter validation, increased algorithm transparency, and assessment of long-term educational impacts are essential. Future research should focus on integrating real-world and simulation data and evaluating the transferability of models to practical clinical environments.
References
Hasan OH, Ayaz A, Khan M, Docherty C, Hashmi P. The need for simulation in surgical education in developing countries. The wind of change. Review article. The Journal of the Pakistan Medical Association. 2019;69(Supl. 1):S62.
Hussein N, Van den Eynde J, Callahan C, Guariento A, Gollmann-Tepeköylü C, Elbatarny M, et al. The use of objective assessments in the evaluation of technical skills in cardiothoracic surgery: a systematic review. Interactive CardioVascular and Thoracic Surgery. 2022;35(3):ivac194.
Asif H, McInnis C, Dang F, Ajzenberg H, Wang PL, Mosa A, et al. Objective structured assessment of technical skill (OSATS) in the surgical skills and technology elective program (SSTEP): comparison of peer and expert raters. The American Journal of Surgery. 2022;223(2):276-9.
Lewandrowski K-U, Bergamashi JP, Telfeian AE, de Carvalho P, Leon J. Training and credentialing standards for minimally invasive spinal surgery techniques: Results of a survey. Pain Physician. 2023;26(1):29-37.
Youssef SC, Aydin A, Canning A, Khan N, Ahmed K, Dasgupta P. Learning surgical skills through video-based education: a systematic review. Surgical Innovation. 2023;30(2):220-38.
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Annals of surgery. 2018;268(1):70-6.
Lam K, Chen J, Wang Z, Iqbal FM, Darzi A, Lo B, et al. Machine learning for technical skill assessment in surgery: a systematic review. NPJ digital medicine. 2022;5(1):24.
Zhou X-Y, Guo Y, Shen M, Yang G-Z. Application of artificial intelligence in surgery. Frontiers of medicine. 2020;14(4):417-30.
Nwoye CI. Deep learning methods for the detection and recognition of surgical tools and activities in laparoscopic videos: Université de Strasbourg; 2021.
Ahmidi N, Tao L, Sefati S, Gao Y, Lea C, Haro BB, et al. A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery. IEEE Transactions on Biomedical Engineering. 2017;64(9):2025-41.
Hashimoto DA, Rosman G, Witkowski ER, Stafford C, Navarette-Welton AJ, Rattner DW, et al. Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy. Annals of surgery. 2019;270(3):414-21.
Caballero D, Sánchez-Margallo JA, Pérez-Salazar MJ, Sánchez-Margallo FM. Applications of artificial intelligence in minimally invasive surgery training: a scoping review. Surgeries. 2025;6(1):7.
Kankanamge D, Wijeweera C, Ong Z, Preda T, Carney T, Wilson M, et al. Artificial intelligence based assessment of minimally invasive surgical skills using standardised objective metrics–A narrative review. The American Journal of Surgery. 2025;241:116074.
Boal MW, Anastasiou D, Tesfai F, Ghamrawi W, Mazomenos E, Curtis N, et al. Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review. British Journal of Surgery. 2024;111(1):znad331.
Kutana S, Bitner DP, Addison P, Chung PJ, Talamini MA, Filicori F. Objective assessment of robotic surgical skills: review of literature and future directions. Surgical endoscopy. 2022;36(6):3698-707.
Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. bmj. 2020;368.
Madani A, Liu Y, Pryor A, Altieri M, Hashimoto DA, Feldman L. SAGES surgical data science task force: enhancing surgical innovation, education and quality improvement through data science. Surgical Endoscopy. 2024;38(7):3489-93.
Andreatta P, Smith CS, Graybill JC, Bowyer M, Elster E. Challenges and opportunities for artificial intelligence in surgery. The Journal of Defense Modeling and Simulation. 2022;19(2):219-27.
Khalid S, Goldenberg M, Grantcharov T, Taati B, Rudzicz F. Evaluation of deep learning models for identifying surgical actions and measuring performance. JAMA network open. 2020;3(3):e201664.
Funke I, Mees ST, Weitz J, Speidel S. Video-based surgical skill assessment using 3D convolutional neural networks. International journal of computer assisted radiology and surgery. 2019;14(7):1217-25.
Pan M, Wang S, Li J, Li J, Yang X, Liang K. An automated skill assessment framework based on visual motion signals and a deep neural network in robot-assisted minimally invasive surgery. Sensors. 2023;23(9):4496.
Lavanchy JL, Zindel J, Kirtac K, Twick I, Hosgor E, Candinas D, et al. Automation of surgical skill assessment using a three-stage machine learning algorithm. Scientific reports. 2021;11(1):5197.
Lukács E, Levendovics R, Haidegger T. Enhancing autonomous skill assessment of robot-assisted minimally invasive surgery: A comprehensive analysis of global and gesture-level techniques applied on the JIGSAWS dataset. Acta Polytech Hung. 2023;20(8):133-53.
Anh NX, Nataraja RM, Chauhan S. Towards near real-time assessment of surgical skills: A comparison of feature extraction techniques. Computer methods and programs in biomedicine. 2020;187:105234.
Ben Hmido S, Abder Rahim H, Keller B, Daams F, Schakel M, Goslings JC, et al. Ethical Pitfalls in AI‐Based Predictive Models in Surgery. World Journal of Surgery. 2025;49(10):2837-45.
Saravi B, Hassel F, Ülkümen S, Zink A, Shavlokhova V, Couillard-Despres S, et al. Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models. Journal of Personalized Medicine. 2022;12(4):509.
Raghavan N, Patel PC, Limon D, Morris MX, Kempenich JW, Rajesh A. Artificial intelligence in surgical education: a 2025 update on adaptive training, feedback, and competency-based education. The American Surgeon™. 2026;92(3):675-86.
Zuluaga L, Rich JM, Gupta R, Pedraza A, Ucpinar B, Okhawere KE, et al., editors. AI-powered real-time annotations during urologic surgery: The future of training and quality metrics. Urologic Oncology: Seminars and Original Investigations; 2024: Elsevier.
Ogbonnaya CN, Li S, Tang C, Zhang B, Sullivan P, Erden MS, et al., editors. Exploring the role of artificial intelligence (AI)-driven training in laparoscopic suturing: a systematic review of skills mastery, retention, and clinical performance in surgical education. Healthcare; 2025: MDPI.
Ledwos N, Mirchi N, Yilmaz R, Winkler-Schwartz A, Sawni A, Fazlollahi AM, et al. Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence. Journal of neurosurgery. 2022;137(4):1160-71.
Winkler-Schwartz A, Bissonnette V, Mirchi N, Ponnudurai N, Yilmaz R, Ledwos N, et al. Artificial intelligence in medical education: best practices using machine learning to assess surgical expertise in virtual reality simulation. Journal of surgical education. 2019;76(6):1681-90.
Bourdillon AT, Garg A, Wang H, Woo YJ, Pavone M, Boyd J. Integration of reinforcement learning in a virtual robotic surgical simulation. Surgical innovation. 2023;30(1):94-102.
Guni A, Varma P, Zhang J, Fehervari M, Ashrafian H. Artificial intelligence in surgery: the future is now. European Surgical Research. 2024;65(1):22-39.
Arjomandi Rad A, Vardanyan R, Athanasiou T, Maessen J, Sardari Nia P. The ethical considerations of integrating artificial intelligence into surgery: a review. Interdisciplinary Cardiovascular and Thoracic Surgery. 2025;40(3):ivae192.
Khan MA. Ethical implications of AI (artificial intelligence) in healthcare mainly focus on surgical procedures: identification of ethical issues of AI in surgical procedures and ranking of ethical issues based on criticality. 2024.
Collins JW, Marcus HJ, Ghazi A, Sridhar A, Hashimoto D, Hager G, et al. Ethical implications of AI in robotic surgical training: a Delphi consensus statement. European urology focus. 2022;8(2):613-22.
Fazlollahi AM, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, Mirchi N, et al. Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students: a randomized clinical trial. JAMA network open. 2022;5(2):e2149008.
Pedrett R, Mascagni P, Beldi G, Padoy N, Lavanchy JL. Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review. Surgical endoscopy. 2023;37(10):7412-24.
Kenig N, Monton Echeverria J, Muntaner Vives A. Artificial intelligence in surgery: a systematic review of use and validation. Journal of clinical medicine. 2024;13(23):7108.
Varghese C, Harrison EM, O’Grady G, Topol EJ. Artificial intelligence in surgery. Nature medicine. 2024;30(5):1257-68.
Xiao J, Wolter NE, Davies JC, Propst EJ, Crowson MG, Siu JM. Artificial Intelligence in Surgical Training and Applications to Otolaryngology: A Scoping Review. The Laryngoscope. 2025;135(10):3520-34.
Kewalramani D, Roman DS, Lagos SA, Rammsy F, Villagran I, Escalona G, et al. Quality matters: Artificial intelligence-based assessment of feedback quality predicts technical skill improvement. Surgery. 2025;187:109625.
Zhang Y, Weng Y, Lund J. Applications of explainable artificial intelligence in diagnosis and surgery. Diagnostics. 2022;12(2):237.
Moglia A, Georgiou K, Georgiou E, Satava RM, Cuschieri A. A systematic review on artificial intelligence in robot-assisted surgery. International Journal of Surgery. 2021;95:106151.
Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, et al. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ. 2022;377:e070904.
Funke I, Mees ST, Weitz J, Speidel S. Video-based surgical skill assessment using 3D convolutional neural networks. International Journal of Computer Assisted Radiology and Surgery. 2019;14(7):1217-25.
Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Medical Education. 2022;22(1):772.
Mascagni P, Vardazaryan A, Alapatt D, Urade T, Emre T, Fiorillo C, et al. Artificial Intelligence for Surgical Safety: Automatic Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy Using Deep Learning. Annals of Surgery. 2022;275(5).
Carstens M, Vasisht S, Zhang Z, Barbur I, Reinke A, Maier-Hein L, et al. Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis. npj Digital Medicine. 2025;9(1):59.
Loftus TJ, Altieri MS, Balch JA, Abbott KL, Choi J, Marwaha JS, et al. Artificial Intelligence–enabled Decision Support in Surgery: State-of-the-art and Future Directions. Annals of Surgery. 2023;278(1).
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial Intelligence in Surgery: Promises and Perils. Annals of Surgery. 2018;268(1).
Leon S, Lee S, Perez JE, Hashimoto DA. Artificial intelligence and the education of future surgeons. The American Journal of Surgery. 2025;246:116257.
Winkler-Schwartz A, Yilmaz R, Mirchi N, Bissonnette V, Ledwos N, Siyar S, et al. Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation. JAMA Network Open. 2019;2(8):e198363-e.
Soleymani A, Li X, Tavakoli M. Chapter 7 - Artificial intelligence in robot-assisted surgery: Applications to surgical skills assessment and transfer. In: Boubaker O, editor. Medical and Healthcare Robotics: Academic Press; 2023. p. 183-200.
Park JJ, Tiefenbach J, Demetriades AK. The role of artificial intelligence in surgical simulation. Front Med Technol. 2022;4:1076755.
Soleymani A, Li X, Tavakoli M. Artificial intelligence in robot-assisted surgery: Applications to surgical skills assessment and transfer. Medical and Healthcare Robotics. 2023:183-200.
Loftus TJ, Altieri MS, Balch JA, Abbott KL, Choi J, Marwaha JS, et al. Artificial intelligence–enabled decision support in surgery: state-of-the-art and future directions. Annals of Surgery. 2023;278(1):51-8.
Khalifa A, Tahhan O, Albazooni M, Saeed M, Hamdi R, Stanners M, et al. Automated and Artificial Intelligence (AI)-Derived Performance Assessment in Surgical Simulation: A Systematic Review. Cureus. 2025;17(12).
Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM, et al. Artificial intelligence and surgical decision-making. JAMA surgery. 2020;155(2):148-58.
Wang Z, Majewicz Fey A. Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. International Journal of Computer Assisted Radiology and Surgery. 2018;13(12):1959-70.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
The journal is licensed under a Attribution 4.0 International (CC BY 4.0).
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution - You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
