Artificial Intelligence and Skills Training in Resident Education

Abstract

Artificial intelligence (AI) is rapidly transforming surgical education. AI is defined by the characteristics of objectivity, live feedback, and adaptive learning environment and is well suited for mentor-led self-directed learning. AI can be used to teach cognitive, psychomotor, and nontechnical skills. With limited supportive data, the use of AI in surgical education should proceed with caution with careful study of the process and its outcomes until more data supporting its use are available.

Keywords

AI - surgical education - cognitive - psychomotor - self-directed learning

Publication History

Article published online:
31 December 2025

• References

• 1 Charokar K, Dulloo P. Self-directed learning theory to practice: a footstep towards the path of being a life-long learner. J Adv Med Educ Prof 2022; 10 (03) 135-144
• 2 Knowles M. Self-directed Learning: A Guide for Learners and Teachers. Toronto, ON: The Adult Education Company; 1975: 14-22
• 3 Aho JM, Ruparel RK, Graham E. et al. Mentor-guided self-directed learning affects resident practice. J Surg Educ 2015; 72 (04) 674-679
• 4 Safir O, Dubrowski A, Hui Y, Backstein D, Carnahan H. Self-directed practice scheduling is equivalent to instructor guided practice when learning a complex surgical skill. Procedia Soc Behav Sci 2010; 2 (02) 792-796
• 5 Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D. Artificial intelligence and surgical education: a systematic scoping review of interventions. J Surg Educ 2022; 79 (02) 500-515
• 6 Goldenberg MG. Surgical artificial intelligence in urology: educational applications. Urol Clin North Am 2024; 51 (01) 105-115
• 7 Fried GM, Varas J, Telem DA. et al. Opportunities and applications of educational technologies in surgical education and assessment. Ann Surg 2025; 281 (01) 34-39
• 8 Triola MM, Burk-Rafel J. Precision medical education. Acad Med 2023; 98 (07) 775-781
• 9 Hanesworth P, Bracken S, Elkington S. A typology for a social justice approach to assessment: learning from universal design and culturally sustaining pedagogy. Teach High Educ 2018; 24 (01) 98-11
• 10 Dai CP, Ke F. Educational applications of artificial intelligence in simulation-based learning: a systematic mapping review. Comput Educ Artif Intell 2022; 3: 100087
• 11 Schaye V, Guzman B, Burk-Rafel J. et al. Development and validation of a machine learning model for automated assessment of resident clinical reasoning documentation. J Gen Intern Med 2022; 37 (09) 2230-2238
• 12 Swiecki Z, Khosravi H, Chen G. et al. Assessment in the age of artificial intelligence. Comput Educ Artif Intell 2022; 3: 100075
• 13 Chauhan A, Khaliq F, Nayak KR. Assessing quality of scenario-based multiple-choice questions in physiology: faculty-generated vs. ChatGPT-generated questions among phase I medical students. Int J Artif Intell Educ 2025; 35: 2315-2344
• 14 Gao Y, Kruger U, Intes X, Schwaitzberg S, De S. A machine learning approach to predict surgical learning curves. Surgery 2020; 167 (02) 321-327
• 15 Fazlollahi AM, Bakhaidar M, Alsayegh A. et al. Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students: a randomized clinical trial. JAMA Netw Open 2022; 5 (02) e2149008
• 16 Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF. The virtual operative assistant: an explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS One 2020; 15 (02) e0229596
• 17 Guru KA, Esfahani ET, Raza SJ. et al. Cognitive skills assessment during robot-assisted surgery: separating the wheat from the chaff. BJU Int 2015; 115 (01) 166-174
• 18 Khalid S, Goldenberg M, Grantcharov T, Taati B, Rudzicz F. Evaluation of deep learning models for identifying surgical actions and measuring performance. JAMA Netw Open 2020; 3 (03) e201664
• 19 Reiley CE, Lin HC, Yuh DD, Hager GD. Review of methods for objective surgical skill evaluation. Surg Endosc 2011; 25 (02) 356-366
• 20 Genovese B, Yin S, Sareh S. et al. Surgical hand tracking in open surgery using a versatile motion sensing system: are we there yet?. Am Surg 2016; 82 (10) 872-875
• 21 Hashimoto DA, Rosman G, Witkowski ER. et al. Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy. Ann Surg 2019; 270 (03) 414-421
• 22 Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg 2018; 268 (01) 70-76
• 23 Abiri A, Askari SJ, Tao A. et al. Suture breakage warning system for robotic surgery. IEEE Trans Biomed Eng 2019; 66 (04) 1165-1171
• 24 Mascagni P, Vardazaryan A, Alapatt D. et al. Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg 2022; 275 (05) 955-961
• 25 Hung AJ, Chen J, Che Z. et al. Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes. J Endourol 2018; 32 (05) 438-444
• 26 Stefanidis D, Hope WW, Korndorffer Jr JR, Markley S, Scott DJ. Initial laparoscopic basic skills training shortens the learning curve of laparoscopic suturing and is cost-effective. J Am Coll Surg 2010; 210 (04) 436-440
• 27 Goldenberg MG, Jung J, Grantcharov TP. Using data to enhance performance and improve quality and safety in surgery. JAMA Surg 2017; 152 (10) 972-973
• 28 Satapathy P, Hermis AH, Rustagi S, Pradhan KB, Padhi BK, Sah R. Artificial intelligence in surgical education and training: opportunities, challenges, and ethical considerations - correspondence. Int J Surg 2023; 109 (05) 1543-1544
• 29 Nyundo M, Umugwaneza N, Bekele A, Chikoya L, Gashegu J, Detry O. Assessment of resource capacity and barriers to effective practice of laparoscopic surgery in training hospitals affiliated with the College of Surgeons of East, Central and Southern Africa (COSECSA). Surg Endosc 2023; 37 (07) 5121-5128
• 30 Alayande BT, Forbes C, Masimbi O. et al. The implementation of simulation-based learning for training undergraduate medical students in essential surgical care across Sub-Saharan Africa: a scoping review. Med Sci Educ 2023; 34 (01) 237-256
• 31 Reynolds CW, Rooney DM, Jeffcoach DR. et al. Evidence supporting performance measures of laparoscopic appendectomy through a novel surgical proficiency assessment tool and low-cost laparoscopic training system. Surg Endosc 2023; 37 (09) 7170-7177
• 32 Reynolds CW, Rooney DM, Jeffcoach DR. et al. Evidence supporting a virtual case-based module to improve cognitive foundation for the management of laparoscopic appendectomy. Global Surg Educ 2024; 3 (01) 21
• 33 Ryder CY, Mott NM, Gross CL. et al. Using artificial intelligence to gauge competency on a novel laparoscopic training system. J Surg Educ 2024; 81 (02) 267-274
• 34 Kim E, Rosenthal LS, Ryder CY. et al. Generalizability of artificial intelligence assessments in laparoscopic surgery simulation. J Surg Res 2025; 309: 249-256
• 35 Cruess SR, Cruess RL. Professionalism and medicine's social contract with society. Virtual Mentor 2004; 6 (04) 185-188
• 36 Gwyer J, Hack L. Education is a social process. J Phys Ther Educ 2012; 26 (03) 3