ARTIFICIAL INTELLIGENCE IN THE PREDICTION AND DIAGNOSIS OF OSTEOMYELITIS: A COMPREHENSIVE REVIEW

Abstract

Osteomyelitis, a bacterial or fungal infection of bone, continues to pose diagnostic challenges due to its insidious onset and often nonspecific clinical presentation. Conventional diagnosis relies on physical examination, biochemical markers, and imaging modalities such as X‑ray, CT, and MRI, which may fail to detect early disease. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising adjunct to clinical decision‑making by improving diagnostic sensitivity, risk stratification, and prognostication. This review synthesizes current advances, evaluates performance metrics of AI methods in osteomyelitis detection and prediction, and identifies key limitations and future directions.