Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Clin Cases. Jun 16, 2026; 14(17): 120192
Published online Jun 16, 2026. doi: 10.12998/wjcc.v14.i17.120192
Published online Jun 16, 2026. doi: 10.12998/wjcc.v14.i17.120192
Artificial intelligence in orthopaedics: Clinical decision support, medical imaging, surgical planning, and outcome prediction
Anirudh Dwajan, Amber Agarwal, Mary Lalhmingmawii, Department of Orthopaedics, All India Institute of Medical Sciences-Bilaspur, Bilaspur 174001, Himachal Pradesh, India
Deepak Ranjan Patro, Department of Orthopaedics, All India Institute of Medical Sciences-Bhubaneswar, Bhubaneswar 751019, Odisha, India
Author contributions: Dwajan A conceived and designed the study and performed literature analysis; Dwajan A and Agarwal A contributed to manuscript drafting and critical revisions; Patro DR contributed to conceptual guidance and scientific revisions; Agarwal A contributed to data interpretation; Lalhmingmawii M contributed to literature review, manuscript editing, and intellectual content revision. All authors approved the final version to publish.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Anirudh Dwajan, MD, Department of Orthopaedics, All India Institute of Medical Sciences-Bilaspur, Kothipura, Bilaspur 174001, Himachal Pradesh, India.
Received: February 24, 2026
Revised: March 6, 2026
Accepted: April 2, 2026
Published online: June 16, 2026
Processing time: 105 Days and 10.6 Hours
Revised: March 6, 2026
Accepted: April 2, 2026
Published online: June 16, 2026
Processing time: 105 Days and 10.6 Hours
Core Tip
Core Tip: Artificial intelligence is rapidly advancing orthopaedic care by improving diagnostic accuracy, surgical planning, risk prediction, and personalised rehabilitation through integration of imaging, clinical, and biomechanical data. Emerging technologies such as decision support systems, robotics, and predictive modelling are enhancing precision and efficiency across subspecialties. Despite promising clinical performance, challenges including dataset bias, limited external validation, regulatory uncertainty, and data privacy concerns must be addressed. Artificial intelligence is best viewed as a clinical adjunct that augments surgeon judgement and supports the transition toward data-driven, precision musculoskeletal medicine.