A 20‑Step Agile Closed‑Loop Diagnostic Workflow for Multimodal Musculoskeletal Imaging: From Clinical Information to Risk Stratification

Bone‑RADS only delivers terminal risk assessment for simple isolated osseous lesions andlacks complete intermediate reasoning chains for broad musculoskeletal disease1. Existingmusculoskeletal artificial intelligence relies on statistical pattern‑matching, and encountersobvious performance bottlenecks facing systemic, infectious, pediatric and rare bone disorders;the bottleneck originates from absence of explicit step‑by‑step reasoning framework instead ofinsufficient image datasets. This study constructs a 20‑step agile closed‑loop multimodaldiagnostic workflow covering full‑spectrum skeletal disorders. The workflow enables forwardreasoning, backward retrospective verification and flexible step‑skipping for real‑worldclinical scenarios. Adapted 47 atomic imaging components2 are introduced as granular featureprimitives to support lesion interpretation. Risk‑stratification tiers are generated as output aftercompleting the whole reasoning pipeline and are not adopted as internal inference kernel. Theworkflow can be applied to routine clinical reading, radiology resident training, and providesinterpretable executable logic for multimodal large‑model artificial intelligence. This is aconceptual method‑oriented framework. Algorithm coding and multi‑center clinical validationare reserved for future investigations.