AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025280060
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REVIEW ARTICLE

An artificial intelligence-assisted comprehensive clinical decision-making guide for total knee arthroplasty

Puja Ravi1 ,  Rahul Kumar2* ,  Kyle Sporn3 ,  Nasif Zaman4 ,  Alireza Tavakkoli4
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1 Department of Biology, University of Michigan, Ann Arbor, Michigan, United States of America
2 Department of Medicine, T.H. Chan School of Medicine, University of Massachusetts, Worcester, Massachusetts, United States of America
3 Department of Medicine, SUNY Upstate Medical University, Norton College of Medicine, Syracuse, New York, United States of America
4 Human–Machine Perception Laboratory, Department of Computer Science and Engineering, College of Engineering, University of Nevada, Reno, Nevada, United States of America
AIH, 025280060 https://doi.org/10.36922/AIH025280060
Received: 8 July 2025 | Revised: 30 August 2025 | Accepted: 11 September 2025 | Published online: 14 October 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

With the demand for total knee arthroplasty (TKA) projected to reach 3.48 million surgeries annually by 2030, leveraging artificial intelligence (AI) is crucial for optimizing outcomes and healthcare efficiency. Hence, we present a systematic approach for integrating AI and machine learning approaches into clinical decision-making for TKAand other orthopedic interventions. This guide outlines evidence-based protocols for various scenarios, from comprehensive pre-operative assessment and risk stratification using natural language processing and biomarker analysis, to intraoperative decision support with computer vision for optimal component positioning. It also covers the detection of early osteoarthritis in athletes through molecular biomarkers and advanced imaging, as well as systematic post-operative monitoring for complication prevention. This guide encompasses chronic pain management, population health screening for early osteoarthritis, acute knee injury assessment in the emergency room, and complex revision TKA planning. Essential technological integrations include ResNet for imaging analysis, Claude 3 for complex reasoning, OpenCV/MediaPipe for biomechanical evaluation, and GPT-4 Turbo for documentation. For the safe, ethical, and efficient application of AI—and ultimately for the improvement of individualized orthopedic care—this framework places a strong emphasis on thorough clinical validation, regulatory compliance, bias mitigation, and economic considerations.

Graphical abstract
Keywords
Total knee arthroplasty
Artificial intelligence
Machine learning
Clinical decision-making
Orthopedic interventions
Risk stratification
Post-operative monitoring
Personalized orthopedic care
Funding
None.
Conflict of interest
The authors declare no conflicts of interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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