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Concerns surrounding application of artificial intelligence in hip and knee arthroplasty : a review of literature and recommendations for meaningful adoption.
Polisetty, Teja S; Jain, Samagra; Pang, Michael; Karnuta, Jaret M; Vigdorchik, Jonathan M; Nawabi, Danyal H; Wyles, Cody C; Ramkumar, Prem N.
Affiliation
  • Polisetty TS; Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Jain S; Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, Texas, USA.
  • Pang M; Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Karnuta JM; Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Vigdorchik JM; Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA.
  • Nawabi DH; Sports Medicine Institute, Hospital for Special Surgery, New York, New York, USA.
  • Wyles CC; Department of Orthopaedic Surgery, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Ramkumar PN; Department of Orthopaedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Bone Joint J ; 104-B(12): 1292-1303, 2022 Dec.
Article in En | MEDLINE | ID: mdl-36453039
ABSTRACT
Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular ("AI/machine learning"), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.Cite this article Bone Joint J 2022;104-B(12)1292-1303.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Orthopedics / Arthroplasty, Replacement, Knee / Augmented Reality Type of study: Guideline / Prognostic_studies Limits: Humans Language: En Journal: Bone Joint J Year: 2022 Document type: Article Affiliation country: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Orthopedics / Arthroplasty, Replacement, Knee / Augmented Reality Type of study: Guideline / Prognostic_studies Limits: Humans Language: En Journal: Bone Joint J Year: 2022 Document type: Article Affiliation country: Estados Unidos