Artificial intelligence in musculoskeletal surgery: Bridging innovation and evidence-based practice

Artificial intelligence (AI) has emerged as one of the most transformative technologies in modern medicine, reshaping how diseases are diagnosed, treated, and monitored.In musculoskeletal (MSK) surgery, AI is rapidly progressing from research laboratories into routine clinical practice through applications in diagnostic imaging, pre-operative planning, robotic-assisted procedures, post-operative monitoring, and clinical workflow optimization.These developments have generated considerable enthusiasm because they promise greater precision, improved efficiency, and increasingly personalized patient care.However, technological innovation alone should not define clinical progress.The increasing availability of machine learning algorithms, deep learning models, computer vision, and generative AI offers unprecedented opportunities to enhance MSK practice.Nevertheless, successful implementation requires more than sophisticated algorithms.Clinical usefulness depends on robust validation, transparency, patient safety, ethical governance, and demonstrable improvements in patient outcomes.The challenge facing orthopedic surgeons is therefore not whether AI should be incorporated into practice, but how to integrate it responsibly while preserving clinical judgment, professional accountability, and evidence-based decision-making.Growing evidence suggests that successful implementation requires thoughtful integration into clinical workflows while ensuring that surgeons remain central to patient care and decision-making.[1][2][3] FROM INNOVATION TO CLINICAL PRACTICEMSK surgery has traditionally relied on the integration of patient history, physical examination, imaging, biomechanics, and surgical expertise.AI introduces an additional analytical dimension by enabling computers to identify complex patterns within large datasets that may not be readily apparent to clinicians.Recent advances in machine learning and deep learning have expanded AI applications across the entire patient journey, including diagnosis, operative planning, intraoperative navigation, rehabilitation, outcome prediction, administrative documentation, and research support.[4][5][6][7]