From the Journals

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 has shown impressive diagnostic performance for prosthetic joint infections (PJI) after total hip or knee arthroplasty, according to a systematic review in the Journal of Orthopaedic Research. PJI affects about 1.7% of patients post-surgery, leading to serious complications and increased healthcare costs. The analysis of 12 studies revealed that various machine learning algorithms demonstrated accuracy levels from acceptable to outstanding, yet issues such as lack of external validation remain. Addressing these challenges could enhance diagnosis and improve patient outcomes.

Original Source(s)

Toward Smarter Diagnosis of Prosthetic Joint Infection

  • by Julia Cipriano, MS, CMPP

  • March 17, 2026

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