Challenges and Limitations of Machine Learning in Total Joint Arthroplasty: Insights from Recent Studies

Abstract

Background: Total joint arthroplasty (TJA) is one of the most successful surgical procedures for patients to improve the quality of life. In recent years, the use of machine learning (ML) in the setting of arthroplasty decision-making has grown. Methods: This article reviewed studies published between 2020 and 2025 that applied ML to TJA, with a focus on the limitations reported by these studies. A search in ScienceDirect identified 220 articles. After screening and full-text assessment, 17 studies met the inclusion criteria, excluding imaging-based research, to focus on predictive models trained on non-image clinical data. Results: The reviewed studies revealed several common limitations, categorised into four groups, including observations and follow-up (30.3% of the studies), dataset quality and design (27.3%), model transferability and generalisation (27.3%), and outcome measurement and interpretation (15.2%). These limitations impact the reliability and real-world relevance of ML models in the context of arthroplasty. This article also provides suggestions to help researchers address these limitations in future studies. Conclusions: This review provides an overview of the potential limitations associated with the development of ML models within the TJA community in order to identify the gaps and challenges to improve the quality of research and possibly decision-making support systems using joint arthroplasty clinical datasets.

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