Heather S Haeberle1, James M Helm2, Sergio M Navarro3, Jaret M Karnuta2, Jonathan L Schaffer2, John J Callaghan4, Michael A Mont5, Atul F Kamath2, Viktor E Krebs2, Prem N Ramkumar2. 1. Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX. 2. Department of Orthopedic Surgery, Machine Learning Arthroplasty Lab, Cleveland Clinic, Cleveland, OH. 3. Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX; Said Business School, University of Oxford, Oxford, United Kingdom. 4. Department of Orthopaedic Surgery, University of Iowa, Iowa City, IA. 5. Department of Orthopaedic Surgery, Lenox Hill, New York, NY.
Abstract
BACKGROUND: Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation. METHODS: In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty. RESULTS: A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models. CONCLUSION: The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty.
BACKGROUND: Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation. METHODS: In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty. RESULTS: A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models. CONCLUSION: The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty.
Authors: Christian Klemt; Akachimere Cosmas Uzosike; John G Esposito; Michael Joseph Harvey; Ingwon Yeo; Murad Subih; Young-Min Kwon Journal: Arch Orthop Trauma Surg Date: 2022-06-29 Impact factor: 3.067