Dwajan A, Patro DR, Agarwal A, Lalhmingmawii M. Artificial intelligence in orthopaedics: Clinical decision support, medical imaging, surgical planning, and outcome prediction. World J Clin Cases 2026; 14(17): 120192 [DOI: 10.12998/wjcc.v14.i17.120192]
Corresponding Author of This Article
Anirudh Dwajan, MD, Department of Orthopaedics, All India Institute of Medical Sciences-Bilaspur, Kothipura, Bilaspur 174001, Himachal Pradesh, India. anirudhdwajan@gmail.com
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Orthopedics
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Dwajan A, Patro DR, Agarwal A, Lalhmingmawii M. Artificial intelligence in orthopaedics: Clinical decision support, medical imaging, surgical planning, and outcome prediction. World J Clin Cases 2026; 14(17): 120192 [DOI: 10.12998/wjcc.v14.i17.120192]
World J Clin Cases. Jun 16, 2026; 14(17): 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, Deepak Ranjan Patro, Amber Agarwal, Mary Lalhmingmawii
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. anirudhdwajan@gmail.com
Received: February 24, 2026 Revised: March 6, 2026 Accepted: April 2, 2026 Published online: June 16, 2026 Processing time: 105 Days and 9.4 Hours
Abstract
Artificial intelligence (AI) is increasingly transforming orthopaedic practice by enhancing diagnostic accuracy, clinical decision-making, surgical planning, and postoperative monitoring. Advances in computational modelling, imaging analysis, and predictive analytics now enable clinicians to process complex multimodal datasets derived from radiological imaging, clinical records, biomechanical parameters, and patient-reported outcomes. These technologies are being applied across multiple orthopaedic subspecialties, including trauma, spine surgery, arthroplasty, sports medicine, oncology, infection, and rehabilitation. AI-driven systems have demonstrated strong performance in fracture detection, implant assessment, tumour characterisation, infection diagnosis, and prediction of surgical outcomes, often matching or exceeding conventional analytical approaches. In operative settings, integration with robotics, navigation platforms, and augmented visualisation systems improves procedural precision, reproducibility, and intraoperative decision support. Predictive models also assist clinicians in risk stratification, patient selection, complication forecasting, and personalised rehabilitation planning. Despite these promising developments, widespread clinical implementation remains limited by challenges related to dataset variability, algorithm transparency, regulatory oversight, cost, and concerns regarding bias and data privacy. Ongoing multicentre validation studies, development of explainable computational frameworks, and global collaboration will be essential for safe and equitable adoption. AI is unlikely to replace orthopaedic clinicians but is expected to function as a powerful adjunct that enhances clinical judgement and supports precision-based musculoskeletal care. Continued technological refinement and responsible integration into clinical workflows will determine its long-term impact on patient outcomes and healthcare delivery.
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.
