Page 17 - AIH-2-2

Artificial Intelligence in Health

REVIEW ARTICLE
Emerging trends and future directions of

machine learning in arthroplasty: A narrative
review

1†
2†
2
2
Jayden K. Simo , Akshar V. Patel * , Ryan C. White , Galo C. Bustamante ,
2
2
Mychael R. Dopirak , Seth Wilson 2 , John S. Barnett 2 , Collin P. Todd ,
2,3
Julie Y. Bishop , Gregory L. Cvetanovich , and Ryan C. Rauck 2,3
2,3
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
† These authors contributed equally Abstract
to this work.
*Corresponding author: Artificial intelligence (AI) is rapidly transforming orthopedic surgery, particularly
Akshar V. Patel in total joint arthroplasty (TJA), offering new possibilities for improving patient
(akshar.patel@osumc.edu)
outcomes. Thus, this narrative review examines the current applications and future
Citation: Simo JK, Patel AV, directions of machine learning (ML) in hip, knee, and shoulder arthroplasty, focusing
White RC, et al. Emerging trends
and future directions of machine on predictive models for clinical outcomes, complications, and patient-reported
learning in arthroplasty: A narrative outcome measures (PROMs). Preoperatively, ML algorithms have shown promise
review. Artif Intell Health. in identifying implants, predicting implant sizes, and assessing implant positioning
2025;2(2):11-28.
doi: 10.36922/aih.3278 on radiographs. In outcome prediction, ML models have been developed to predict
PROMs, readmissions, length of stay, and healthcare costs associated with TJA. By
Received: March 26, 2024 analyzing large datasets to generate personalized predictions for patients, these
1st revised: May 15, 2024 models represent a novel approach to assist clinicians in individualized patient
2nd revised: July 8, 2024 decision-making. Furthermore, AI has shown promise in predicting specific post-
operative complications, such as dislocations, implant loosening, and prolonged
3rd revised: July 16, 2024 opioid use, highlighting its value in improving surgical planning and patient
4th revised: July 31, 2024 management. Looking ahead, AI holds the potential to revolutionize orthopedic
5th revised: September 9, 2024 surgery by equipping clinicians with valuable tools to enhance decision-making
and improve patient outcomes. However, the current efforts are shadowed by the
Accepted: September 9, 2024 challenges of transparency and validation of AI models. As AI continues to find utility
Published online: January 8, 2025 in orthopedic clinics and operating rooms, efforts to enhance transparency and
Copyright: © 2025 Author(s). validate models will be crucial in realizing its full potential in orthopedic surgery.
This is an Open-Access article
distributed under the terms of the
Creative Commons Attribution Keywords: Machine learning; Arthroplasty; Orthopedic surgery; Surgical planning;
License, permitting distribution, Implant identification; Radiographic imaging
and reproduction in any medium,
provided the original work is
properly cited.
Publisher’s Note: AccScience 1. Introduction
Publishing remains neutral with
regard to jurisdictional claims in
published maps and institutional Artificial intelligence (AI) has emerged as a new modality to augment traditional
affiliations. approaches to medicine over the last few years. AI encompasses several domains, such

Volume 2 Issue 2 (2025) 11 doi: 10.36922/aih.3278