Early diagnosis of axial spondyloarthritis in primary care using multi-agent systems

Axial spondyloarthritis (axSpA) is an inflammatory disease marked by chronic low back pain, with a global average diagnostic delay of 6.7 years. Early diagnosis is crucial for improving prognosis and reducing disability rates, yet primary care physicians (PCPs) may find it challenging to ensure timely recognition and referrals. This study developed and validated Spondyloarthritis Agents (SpAgents), an early diagnostic system based on a multi-agent framework integrating large language models (LLMs) and imaging models. The SpAgents framework includes PlannerAgent, DataAgent, ToolAgent, and DoctorAgent, supported by long-term memory for dynamic knowledge updates. We enrolled 596 patients, dividing 545 from one hospital into a training dataset (n = 359) and a validation dataset (n = 186), along with an independent cohort of 51 patients from five additional hospitals for testing. SpAgents demonstrated strong diagnostic performance, achieving sensitivity of 0.8615 and specificity of 0.8000 during validation, and 0.9375 and 0.7368 during testing. SpAgents exhibited significantly higher sensitivity (0.9400) and accuracy (0.8600) than both PCPs and junior rheumatologists, with overall performance equivalent to that of senior rheumatologists. Under SpAgents-assisted diagnosis, both PCPs and junior rheumatologists showed marked improvements in sensitivity and accuracy. SpAgents effectively enhance early axSpA identification among PCPs, offering an innovative solution to reduce diagnostic delays.