A new multi-network fusion object detection framework showed superior diagnostic performance for scalp psoriasis compared with dermatologists, according to a study in Skin Research and Technology. Honghai Ji and colleagues analyzed dermoscopic images from scalp psoriasis cases, using cases of scalp seborrheic dermatitis as controls. They used dermoscopic images and major patterns of both conditions to develop a multi-network fusion object detection framework based on Faster R-CNN and contrast-limited adaptive histogram equalization. The investigators collected a total of 1,876 demoscopic images, including 1,218 for scalp psoriasis and 658 for seborrheic dermatitis. The multi-network fusion framework’s diagnostic performance for scalp psoriasis showed 91% accuracy, 89.5% specificity, 91.0% sensitivity, and a Youden index of 0.805. For differentiating dermoscopic patterns of both diseases, the AI showed 89.9% accuracy, 97.7% specificity, 89.9% sensitivity, and a Youden index of 0.876. Compared with five dermatologists, the fusion framework demonstrated superior diagnostic performance, indicating a significant advancement in the accuracy and efficiency of scalp psoriasis diagnosis using AI-enhanced dermoscopy.