5 Key Takeaways

• 1

  Machine learning models show high performance in diagnosing prosthetic joint infections (PJI) but lack external validation.

• 2

  PJI affects up to 1.7% of patients post-arthroplasty, leading to significant morbidity and increased healthcare costs.

• 3

  The review included 12 studies, primarily using retrospective data, with sample sizes ranging from 20 to 17,165 surgeries.

• 4

  Reported AUC values for diagnostic performance ranged from 0.68 to 0.993, indicating varying levels of accuracy.

• 5

  The authors call for multicenter studies and standardized data to enhance the robustness and clinical applicability of machine learning models.