Enhancing Diagnosis of Psoriasis and Inflammatory Skin Diseases: A Spatially Aligned Multimodal Model Integrating Clinical and Dermoscopic Images.

Psoriasis is a chronic inflammatory disease with significant physical and psychological impacts. To overcome the limitations of single-modality artificial intelligence models in diagnosing inflammatory skin diseases, we propose a multimodal framework-spatial alignment multimodal contrastive learning-integrating dermoscopic and clinical photographs and trained on the developed Peking Union Medical College Hospital - Inflammatory Skin Diseases dataset, which includes 8 inflammatory skin diseases. On the Peking Union Medical College Hospital - Inflammatory Skin Diseases dataset, our model achieved classification accuracies of 0.822 for an 8-class classification task and 0.911 for a binary classification task, outperforming single-modality models and simple fusion approaches. Furthermore, on the publicly available Derm7pt dataset, the proposed model surpassed 11 state-of-the-art multimodal methods, achieving an accuracy of 0.807. When used for diagnostic assistance, the model significantly improved the diagnostic accuracy of 20 dermatologists, increasing from 0.775 to 0.890 (P < .05). Among 110 test cases, 87 (79.1%) showed improved diagnostic accuracy. The model demonstrated notable enhancements in diagnosing specific conditions, such as lichen planus, acne, rosacea, and morphea. Validation using heatmaps confirmed that the model's attention aligned with key lesion features in most cases. This study introduces a multimodal model that bridges scale discrepancies between dermoscopic and clinical photographs, offering an efficient tool for diagnosing psoriasis and other inflammatory skin diseases.