Diagnostic Concordance Across Text and Image Workflows in Nine Consumer Multimodal Artificial Intelligence Systems for Cutaneous Vascular Anomalies: A Cross-Sectional Audit

Background/Objectives: The reliability of widely accessible multimodal artificial intelligence systems for rare or complex cutaneous vascular anomalies is uncertain. We evaluated diagnostic concordance and expert-rated management outputs across clinical-summary text, clinical-summary text plus images, and image-only workflows.

Methods

In this cross-sectional audit, a purposively assembled, nonconsecutive challenge set of 511 published cases was evaluated with nine consumer web systems in new-conversation first-response sessions (13,797 final score records). Outputs were classified as top-1 concordance with the source-reported diagnosis, differential-list-only inclusion, or omission. Two vascular-anomaly specialists independently rated expected management appropriateness and safety on 5-point scales. Paired comparisons used exact McNemar, Friedman, and paired Wilcoxon tests.