AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.3278
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REVIEW ARTICLE

Emerging trends and future directions of machine learning in arthroplasty: A narrative review

Jayden K. Simo1† ,  Akshar V. Patel2†* ,  Ryan C. White2 ,  Galo C. Bustamante2 ,  Mychael R. Dopirak2 ,  Seth Wilson2 ,  John S. Barnett2 ,  Collin P. Todd2 ,  Julie Y. Bishop2,3 ,  Gregory L. Cvetanovich2,3 ,  Ryan C. Rauck2,3
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1 Department of Health Sciences, School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, Ohio, United States of America
2 Department of Orthopedics, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America
3 Department of Orthopedics, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
AIH, 3278 https://doi.org/10.36922/aih.3278
Submitted: 26 March 2024 | Revised: 9 September 2024 | Accepted: 9 September 2024 | Published: 8 January 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Artificial intelligence (AI) is rapidly transforming orthopedic surgery, particularly in total joint arthroplasty (TJA), offering new possibilities for improving patient outcomes. Thus, this narrative review examines the current applications and future directions of machine learning (ML) in hip, knee, and shoulder arthroplasty, focusing on predictive models for clinical outcomes, complications, and patient-reported outcome measures (PROMs). Preoperatively, ML algorithms have shown promise in identifying implants, predicting implant sizes, and assessing implant positioning on radiographs. In outcome prediction, ML models have been developed to predict PROMs, readmissions, length of stay, and healthcare costs associated with TJA. By analyzing large datasets to generate personalized predictions for patients, these models represent a novel approach to assist clinicians in individualized patient decision-making. Furthermore, AI has shown promise in predicting specific post-operative complications, such as dislocations, implant loosening, and prolonged opioid use, highlighting its value in improving surgical planning and patient management. Looking ahead, AI holds the potential to revolutionize orthopedic surgery by equipping clinicians with valuable tools to enhance decision-making and improve patient outcomes. However, the current efforts are shadowed by the challenges of transparency and validation of AI models. As AI continues to find utility in orthopedic clinics and operating rooms, efforts to enhance transparency and validate models will be crucial in realizing its full potential in orthopedic surgery.

Keywords
Machine learning
Arthroplasty
Orthopedic surgery
Surgical planning
Implant identification
Radiographic imaging
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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