Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review

Heather S. Haeberle, James M. Helm, Sergio M. Navarro, Jaret M. Karnuta, Jonathan L. Schaffer, John J. Callaghan, Michael A. Mont, Atul F. Kamath, Viktor E. Krebs, Prem N. Ramkumar

Research output: Contribution to journalReview articlepeer-review

107 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2201-2203
Number of pages3
JournalThe Journal of Arthroplasty
Volume34
Issue number10
DOIs
StatePublished - Oct 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

  • arthroplasty
  • big data
  • machine learning
  • remote monitoring
  • value

Fingerprint

Dive into the research topics of 'Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review'. Together they form a unique fingerprint.

Cite this