Automating Radiographic Damage Assessment in Rheumatoid Arthritis: A Narrative Review of Current Status and Future Perspectives

Rheumatoid arthritis (RA) is a chronic autoimmune disease leading to joint damage, which is typically quantified using Sharp/van der Heijde (SvH) scores on hand and foot Xrays. Deep learning–based artificial intelligence (AI) enables automated SvH scoring through (1) global models that process whole radiographs and (2) perjoint pipelines that first localize joints and then score erosions and joint space narrowing with greater interpretability. Remaining challenges include limited expertlabeled data, severe class imbalance, and rater variability, which restrict the robustness and generalizability of these algorithms. Emerging strategies—such as semi/selfsupervised pseudolabeling, advanced segmentation architectures, and symmetryaware modeling—combined with AI deployed as a calibrated “second reader,” may improve sensitivity to subtle progression while maintaining essential human oversight. Harnessing AI could deliver more accurate and efficient radiographic assessment, thereby supporting better research as well as clinical care for patients with RA.