Obtaining and Maintaining Surgical Expertise for the Postgraduate Surgeon and the Impact of Artificial Intelligence in Modern Surgery

Abstract

Surgical training requires years of structured grueling hands-on experience and evidence-based practice, all of which become unstructured at the end of training. The focus on achieving competence then transitions to obtaining expertise. Developing a habit of skills maintenance, deliberate practice, and evidence-based patient care is essential to attain surgical expertise. In addition, the pace of rapid medical advances requires the adoption of new techniques and technologies to maintain expertise. The most pressing being the launch of artificial intelligence in all aspects of our lives, and the impact that it already has and will have in our surgical practices.

Keywords

continuous education - skill maintenance - artificial intelligence

Publication History

Article published online:
04 December 2025

• References

• 1 Patil NG, Cheng SW, Wong J. Surgical competence. World J Surg 2003; 27 (08) 943-947
• 2 Montgomery KB, Mellinger JD, Lindeman B. Entrustable professional activities in surgery: a review. JAMA Surg 2024; 159 (05) 571-577
• 3 Birkmeyer JD, Finks JF, O'Reilly A. et al; Michigan Bariatric Surgery Collaborative. Surgical skill and complication rates after bariatric surgery. N Engl J Med 2013; 369 (15) 1434-1442
• 4 Sachdeva AK. Acquiring and maintaining lifelong expertise in surgery. Surgery 2020; 167 (05) 787-792
• 5 Ericsson KA. Acquisition and maintenance of medical expertise: a perspective from the expert-performance approach with deliberate practice. Acad Med 2015; 90 (11) 1471-1486
• 6 Hu YY, Peyre SE, Arriaga AF. et al. Postgame analysis: using video-based coaching for continuous professional development. J Am Coll Surg 2012; 214 (01) 115-124
• 7 Sachdeva AK. The new paradigm of continuing education in surgery. Arch Surg 2005; 140 (03) 264-269
• 8 Sachdeva AK. Surgical education to improve the quality of patient care: the role of practice-based learning and improvement. J Gastrointest Surg 2007; 11 (11) 1379-1383
• 9 Canal DF, Torbeck L, Djuricich AM. Practice-based learning and improvement: a curriculum in continuous quality improvement for surgery residents. Arch Surg 2007; 142 (05) 479-482 , discussion 482–483
• 10 McGaghie WC. Research opportunities in simulation-based medical education using deliberate practice. Acad Emerg Med 2008; 15 (11) 995-1001
• 11 Zendejas B, Cook DA, Bingener J. et al. Simulation-based mastery learning improves patient outcomes in laparoscopic inguinal hernia repair: a randomized controlled trial. Ann Surg 2011; 254 (03) 502-509 , discussion 509–511
• 12 Leung JS, Brar M, Eltorki M. et al. Development of an in situ simulation-based continuing professional development curriculum in pediatric emergency medicine. Adv Simul (Lond) 2020; 5 (01) 12
• 13 Sachdeva AK, Russell TR. Safe introduction of new procedures and emerging technologies in surgery: education, credentialing, and privileging. Surg Oncol Clin N Am 2007; 16 (01) 101-114
• 14 Knol J, Keller DS. Cognitive skills training in digital era: A paradigm shift in surgical education using the TaTME model. Surgeon 2019; 17 (01) 28-32
• 15 Stefanidis D, Heniford BT. The formula for a successful laparoscopic skills curriculum. Arch Surg 2009; 144 (01) 77-82 , discussion 82
• 16 Anton NE, Beane J, Yurco AM. et al. Mental skills training effectively minimizes operative performance deterioration under stressful conditions: results of a randomized controlled study. Am J Surg 2018; 215 (02) 214-221
• 17 Anton NE, Bean EA, Hammonds SC, Stefanidis D. Application of mental skills training in surgery: a review of its effectiveness and proposed next steps. J Laparoendosc Adv Surg Tech A 2017; 27 (05) 459-469
• 18 Cragg J, Mushtaq F, Lal N. et al. Surgical cognitive simulation improves real-world surgical performance: randomized study. BJS Open 2021; 5 (03) zrab003
• 19 Wickramasinghe D, Vincent J. The use of deliberate practice in simulation-based surgical training for laparoscopic surgery - a systematic review. BMC Med Educ 2025; 25 (01) 1047
• 20 Armour Forse R, Bramble JD, McQuillan R. Team training can improve operating room performance. Surgery 2011; 150 (04) 771-778
• 21 Reznick RK, MacRae H. Teaching surgical skills–changes in the wind. N Engl J Med 2006; 355 (25) 2664-2669
• 22 Ericsson KA, Harwell KW. Deliberate practice and proposed limits on the effects of practice on the acquisition of expert performance: why the original definition matters and recommendations for future research. Front Psychol 2019; 10: 2396
• 23 Bond WF, Lammers RL, Spillane LL. et al; Society for Academic Emergency Medicine Simulation Task Force. The use of simulation in emergency medicine: a research agenda. Acad Emerg Med 2007; 14 (04) 353-363
• 24 Elnikety S, Badr E, Abdelaal A. Surgical training fit for the future: the need for a change. Postgrad Med J 2022; 98 (1165): 820-823
• 25 Evans CH, Schenarts KD. Evolving educational techniques in surgical training. Surg Clin North Am 2016; 96 (01) 71-88
• 26 Davis DA, Mazmanian PE, Fordis M, Van Harrison R, Thorpe KE, Perrier L. Accuracy of physician self-assessment compared with observed measures of competence: a systematic review. JAMA 2006; 296 (09) 1094-1102
• 27 Bonrath EM, Dedy NJ, Gordon LE, Grantcharov TP. Comprehensive surgical coaching enhances surgical skill in the operating room: a randomized controlled trial. Ann Surg 2015; 262 (02) 205-212
• 28 Greenberg CC, Ghousseini HN, Pavuluri Quamme SR. et al; Wisconsin Surgical Coaching Program. A statewide surgical coaching program provides opportunity for continuous professional development. Ann Surg 2018; 267 (05) 868-873
• 29 Shubeck SP, Kanters AE, Sandhu G, Greenberg CC, Dimick JB. Dynamics within peer-to-peer surgical coaching relationships: Early evidence from the Michigan Bariatric Surgical Collaborative. Surgery 2018; 164 (02) 185-188
• 30 Pradarelli JC, Yule S, Panda N. et al. Surgeons' coaching techniques in the surgical coaching for operative performance enhancement (SCOPE) program. Ann Surg 2022; 275 (01) e91-e98
• 31 Willemot L, Lee MJ, Mulford J. Introduction to surgical coaching. ANZ J Surg 2023; 93 (03) 487-492
• 32 Greenberg CC, Dombrowski J, Dimick JB. Video-based surgical coaching: an emerging approach to performance improvement. JAMA Surg 2016; 151 (03) 282-283
• 33 Singh P, Aggarwal R, Tahir M, Pucher PH, Darzi A. A randomized controlled study to evaluate the role of video-based coaching in training laparoscopic skills. Ann Surg 2015; 261 (05) 862-869
• 34 COVIDSurg Collaborative. Elective surgery cancellations due to the COVID-19 pandemic: global predictive modelling to inform surgical recovery plans. Br J Surg 2020; 107 (11) 1440-1449
• 35 Jenison EL, Gil KM, Lendvay TS, Guy MS. Robotic surgical skills: acquisition, maintenance, and degradation. JSLS 2012; 16 (02) 218-228
• 36 Stulberg JJ, Huang R, Kreutzer L. et al. Association between surgeon technical skills and patient outcomes. JAMA Surg 2020; 155 (10) 960-968
• 37 Nofi C, Roberts B, Demyan L. et al. A survey of the impact of the COVID-19 crisis on skill decay among surgery and anesthesia residents. J Surg Educ 2022; 79 (02) 330-341
• 38 Der B, Sanford D, Hakim R, Vanstrum E, Nguyen JH, Hung AJ. Efficiency and accuracy of robotic surgical performance decayed among urologists during COVID-19 shutdown. J Endourol 2021; 35 (06) 888-890
• 39 Gallagher AG, Seymour NE, Jordan-Black JA, Bunting BP, McGlade K, Satava RM. Prospective, randomized assessment of transfer of training (ToT) and transfer effectiveness ratio (TER) of virtual reality simulation training for laparoscopic skill acquisition. Ann Surg 2013; 257 (06) 1025-1031
• 40 Flink BJ, Pryor AD. The Role of Surgical Societies in Quality. In: Romanelli JR, Dort JM, Kowalski RB, Sinha P. eds. The SAGES Manual of Quality, Outcomes and Patient Safety. Cham: Springer International Publishing; 2022: 195-203
• 41 Langenfeld SJ. The role of surgical societies in surgical leadership and career development. Semin Colon Rectal Surg 2025; 36 (02) 101107
• 42 Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg 2018; 268 (01) 70-76
• 43 Avram MF, Lazăr DC, Mariş MI, Olariu S. Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer. Front Oncol 2023; 13: 1116761
• 44 Bergquist SL, Brooks GA, Keating NL, Landrum MB, Rose S. Classifying lung cancer severity with ensemble machine learning in health care claims data. Proc Mach Learn Res 2017; 68: 25-38
• 45 Soguero-Ruiz C, Hindberg K, Rojo-Alvarez JL. et al. Support vector feature selection for early detection of anastomosis leakage from bag-of-words in electronic health records. IEEE J Biomed Health Inform 2016; 20 (05) 1404-1415
• 46 Monsalve-Torra A, Ruiz-Fernandez D, Marin-Alonso O, Soriano-Payá A, Camacho-Mackenzie J, Carreño-Jaimes M. Using machine learning methods for predicting inhospital mortality in patients undergoing open repair of abdominal aortic aneurysm. J Biomed Inform 2016; 62: 195-201
• 47 Kenngott HG, Wagner M, Nickel F. et al. Computer-assisted abdominal surgery: new technologies. Langenbecks Arch Surg 2015; 400 (03) 273-281
• 48 Volkov M, Hashimoto DA, Rosman G, Meireles OR, Rus D. Machine learning and coresets for automated real-time video segmentation of laparoscopic and robot-assisted surgery. Paper presented at: 2017 IEEE International Conference on Robotics and Automation (ICRA); May 29–June 3, 2017, 2017.
• 49 Nopour R, Shanbehzadeh M, Kazemi-Arpanahi H. Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer. Med J Islam Repub Iran 2021; 35: 44
• 50 Vlamou E, Papadopoulos B. Fuzzy logic systems and medical applications. AIMS Neurosci 2019; 6 (04) 266-272
• 51 Lai T-J, Roxburgh C, Boyd KA, Bouttell J. Clinical effectiveness of robotic versus laparoscopic and open surgery: an overview of systematic reviews. BMJ Open 2024; 14 (09) e076750
• 52 Wee IJY, Kuo LJ, Ngu JC. A systematic review of the true benefit of robotic surgery: ergonomics. Int J Med Robot 2020; 16 (04) e2113
• 53 Rojas Burbano JC, Ruiz NI, Rojas Burbano GA, Guacho Inca JS, Arragan Lezama CA, González MS. Robot-assisted surgery: current applications and future trends in general surgery. Cureus 2025; 17 (04) e82318
• 54 Ciuti G, Webster RJ, Kwok K-W, Menciassi A. Robotic surgery. Nat Rev Bioeng 2025; 3 (07) 565-578
• 55 Soguero-Ruiz C, Hindberg K, Mora-Jiménez I. et al. Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods. J Biomed Inform 2016; 61: 87-96
• 56 Park SH, Park HM, Baek KR, Ahn HM, Lee IY, Son GM. Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery. World J Gastroenterol 2020; 26 (44) 6945-6962
• 57 Igaki T, Kitaguchi D, Kojima S. et al. Artificial intelligence-based total mesorectal excision plane navigation in laparoscopic colorectal surgery. Dis Colon Rectum 2022; 65 (05) e329-e333
• 58 Mayer H, Gomez F, Wierstra D, Nagy I, Knoll A, Schmidhuber J. A system for robotic heart surgery that learns to tie knots using recurrent neural networks. Paper presented at: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems; October 9–15, 2006, 2006.
• 59 Kassahun Y, Yu B, Tibebu AT. et al. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions. Int J Comput Assist Radiol Surg 2016; 11 (04) 553-568
• 60 Shademan A, Decker RS, Opfermann JD, Leonard S, Krieger A, Kim PC. Supervised autonomous robotic soft tissue surgery. Sci Transl Med 2016; 8 (337) 337ra64
• 61 Langerman A, Grantcharov TP. Are we ready for our close-up? Why and how we must embrace video in the OR. Ann Surg 2017; 266 (06) 934-936
• 62 Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024; 30 (05) 1257-1268
• 63 Mirnezami R, Ahmed A. Surgery 3.0, artificial intelligence and the next-generation surgeon. Br J Surg 2018; 105 (05) 463-465
• 64 Wijnberge M, Geerts BF, Hol L. et al. Effect of a machine learning-derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: the HYPE randomized clinical trial. JAMA 2020; 323 (11) 1052-1060
• 65 Bhattacharya K, Bhattacharya N, Kumar S, Yagnik VD, Garg P, Choudhary PR. Artificial intelligence in predicting postoperative surgical complications. Indian J Surg 2025; 87 (01) 5-9
• 66 Bonde A, Varadarajan KM, Bonde N. et al. Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study. Lancet Digit Health 2021; 3 (08) e471-e485
• 67 Safavi KC, Khaniyev T, Copenhaver M. et al. Development and validation of a machine learning model to aid discharge processes for inpatient surgical care. JAMA Netw Open 2019; 2 (12) e1917221
• 68 Azadi S, Green IC, Arnold A, Truong M, Potts J, Martino MA. Robotic surgery: the impact of simulation and other innovative platforms on performance and training. J Minim Invasive Gynecol 2021; 28 (03) 490-495
• 69 Leifman G, Golany T, Rivlin E, Khoury W, Assalia A, Reissman P. Real-time artificial intelligence validation of critical view of safety in laparoscopic cholecystectomy. Intell Based Med 2024; 10: 100153
• 70 Igaki T, Kitaguchi D, Matsuzaki H. et al. Automatic surgical skill assessment system based on concordance of standardized surgical field development using artificial intelligence. JAMA Surg 2023; 158 (08) e231131
• 71 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
• 72 Kiyasseh D, Ma R, Haque TF. et al. A vision transformer for decoding surgeon activity from surgical videos. Nat Biomed Eng 2023; 7 (06) 780-796
• 73 Leon S, Lee S, Perez JE, Hashimoto DA. Artificial intelligence and the education of future surgeons. Am J Surg 2025; 246: 116257