Toward Smarter Diagnosis of Prosthetic Joint Infection

Artificial intelligence models show promise but face key validation and design limitations

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Machine learning models show high performance in diagnosing prosthetic joint infections (PJI) but lack external validation.
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PJI affects up to 1.7% of patients post-arthroplasty, leading to significant morbidity and increased healthcare costs.
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The review included 12 studies, primarily using retrospective data, with sample sizes ranging from 20 to 17,165 surgeries.
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Reported AUC values for diagnostic performance ranged from 0.68 to 0.993, indicating varying levels of accuracy.
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The authors call for multicenter studies and standardized data to enhance the robustness and clinical applicability of machine learning models.

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