Application of artificial intelligence in the diagnosis of spinal infectious diseases: A review of diagnostic and differential diagnostic performance

Spinal infectious diseases can lead to severe disability and adverse outcomes without timely and accurate diagnosis and treatment. Artificial intelligence (AI) and deep learning (DL) have brought new progress to imaging-based diagnosis of spinal infectious diseases. This review aimed to evaluate the diagnostic and differential diagnostic performance of AI models for spinal infectious diseases. A systematic literature search was performed in PubMed, Web of Science, and Embase for studies published between 2018 and 2026. Eligible studies focused on AI or DL models for detecting or differentiating spinal infectious diseases, and data including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were extracted and analyzed. A total of eight studies were included. AI models showed generally favorable efficacy in the identification and differential diagnosis of spinal infections. For differentiating tuberculous spondylitis (TS) from brucellar spondylitis (BS), sensitivity ranged from 0.588 to 0.940 and specificity from 0.679 to 0.964. Most models were based on magnetic resonance imaging (MRI) or computed tomography (CT) and achieved high diagnostic accuracy, but the majority were retrospective, single-centered, and lacked large-scale external validation. AI serves as a reliable auxiliary tool for the rapid and accurate diagnosis of spinal infectious diseases, especially when combined with advanced imaging techniques. However, further multicenter, prospective validation is required before widespread clinical application. AI should complement rather than replace clinical expertise in routine diagnostic workflows.