Artificial Intelligence (AI) as a Catalyst for Orthopedic Residency Training

Document Type : EDITORIAL

Authors

Emergency Care Promotion Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran- Department of Orthopedic Surgery, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

10.22038/abjs.2025.91626.4155

Abstract

Orthopedic residency training, despite its critical importance, faces significant challenges such as high costs and resource limitations, particularly in developing nations. This paper explores the transformative potential of Artificial Intelligence (AI) as a catalyst to address these challenges and enhance the quality of orthopedic surgical education. AI can improve the training process through several innovative applications. AI-powered adaptive learning platforms, by analyzing individual residents' performance, offer personalized educational pathways. Additionally, the integration of AI with Virtual Reality (VR) simulators provides a safe and repeatable environment for practicing complex surgeries, offering immediate and objective feedback that may surpass traditional supervision. However, the implementation of these technologies faces significant barriers, including high costs, regulatory uncertainties, privacy concerns, and the "black-box" nature of AI models. Strategic, interdisciplinary collaboration among healthcare professionals, engineers, and policymakers can overcome these barriers.

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Main Subjects

References

  1. Frenk J, Chen L, Bhutta ZA, et al. Health professionals for a new century: transforming education to strengthen health systems in an interdependent world. The Lancet. 2010;376(9756):1923-1958. Doi: 10.1016/S0140-6736(10)61854-5
  2. Moallem SMHM. Artificial Intelligence and the State of the Art of Orthopedic Surgery. Arch Bone Jt Surg. 2025;13(10):668-669. doi:10.22038/ABJS.2025.90701.4108
  3. Lisacek-Kiosoglous AB, Powling AS, Fontalis A, Gabr A, Mazomenos E, Haddad FS. Artificial intelligence in orthopaedic surgery: exploring its applications, limitations, and future direction. Bone & joint research. 2023;12(7):447-454. DOI: 10.1302/2046-3758.127.BJR-2023-0111.R1
  4. Ghouchani A, Ebrahimzadeh MH. AI Revolution in Orthopedic Biomechanics: From Fracture Classification to Real-Time Simulations. Arch Bone Jt Surg. 2025;13(10):607-610. doi:10.22038/ABJS.2025.88346.4012
  5. Peng H, Ma S, Spector JM. Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments. 2019;6(1):1-14. Doi.org/10.1186/s40561-019-0089-y
  6. Mao RQ, Lan L, Kay J, Lohre R, Ayeni OR, Goel DP. Immersive virtual reality for surgical training: a systematic review. Journal of Surgical Research. 2021;268:40-58. Doi: 10.1016/j.jss.2021.06.045
  7. Dick L, Boyle CP, Skipworth RJ, Smink DS, Tallentire VR, Yule S. Automated analysis of operative video in surgical training: scoping review. BJS open. 2024;8(5):zrae124. Doi: 10.1093/bjsopen/zrae124
  8. Koucheki R, Lex JR, Brock M, Goel DP. Integrating Artificial Intelligence and Virtual Reality in Orthopedic Surgery: A Comprehensive Review. Hss j. Jun 17 2025:15563316251345479. doi:10.1177/15563316251345479
  9. Yilmaz R, Bakhaidar M, Alsayegh A, et al. Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial. Scientific Reports. 2024/07/02 2024;14(1):15130. doi:10.1038/s41598-024-65716-8
  10. Brügge E, Ricchizzi S, Arenbeck M, et al. Large language models improve medical students' clinical decision-making through patient simulation and structured feedback: a randomized controlled trial. BMC Med Educ. Nov 28 2024;24(1):1391. doi:10.1186/s12909-024-06399-7
  11. Bala W, Li H, Moon J, Trivedi H, Gichoya J, Balthazar P. Enhancing radiology training with GPT-4: Pilot analysis of automated feedback in trainee preliminary reports. Curr Probl Diagn Radiol. Mar-Apr 2025;54(2):151-158. doi:10.1067/j.cpradiol.2024.08.003
  12. Khani Y, Bisadi A, Salmani A, et al. Can Artificial Intelligence Reliably and Accurately Measure Lower Limb Alignment: A Systematic Review and Meta-Analysis. Arch Bone Jt Surg. 2025;13(7):383-394. doi:10.22038/ABJS.2025.84846.3864
  13. Alba C, Xue B, Abraham J, Kannampallil T, Lu C. The foundational capabilities of large language models in predicting postoperative risks using clinical notes. npj Digital Medicine. 2025/02/11 2025;8(1):95. doi:10.1038/s41746-025-01489-2
  14. Chung P, Fong CT, Walters AM, Aghaeepour N, Yetisgen M, O’Reilly-Shah VN. Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication. JAMA Surgery. 2024;159(8):928-937. doi:10.1001/jamasurg.2024.1621
  15. Paranjape K, Schinkel M, Panday RN, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR medical education. 2019;5(2):e16048. Doi: 10.2196/16048
  16. Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ digital medicine. 2023;6(1):120. Doi: 10.1038/s41746-023-00873-0
The Archives of Bone and Joint Surgery
Volume 13, Issue 12
December 2025
Pages 773-775

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History

  • Receive Date: 05 October 2025
  • Revise Date: 23 October 2025
  • Accept Date: 10 November 2025

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