Core Viewpoint - The integration of artificial intelligence (AI) in the diagnosis and management of skin diseases is urgent, as current AI models are limited to isolated tasks and lack the ability to integrate various data types and imaging modalities, reducing their practical applicability in clinical settings [1][6]. Group 1: AI in Dermatology - Dermatology is complex, encompassing a wide range of conditions from common skin diseases to life-threatening malignancies, necessitating a comprehensive, patient-centered approach that integrates various clinical workflows [2]. - The recent study published by the Monash University team introduced a multimodal vision foundation model for clinical dermatology named PanDerm, which achieved state-of-the-art performance across 28 benchmark tests, surpassing clinical doctors in early melanoma detection and improving diagnostic accuracy for non-dermatologists [3][12]. Group 2: Development of PanDerm - PanDerm is a universal, multimodal dermatological foundation model designed to integrate multiple imaging modalities, pre-trained on over 2 million images from 11 institutions across four imaging types, demonstrating superior data scalability and training efficiency compared to existing self-supervised algorithms [8][10]. - The model's pre-training involved self-supervised learning techniques, enabling comprehensive analysis of patients across various clinical workflows by achieving unified representation learning of full-body skin imaging and clinical images [11]. Group 3: Clinical Performance and Impact - In evaluations, PanDerm outperformed existing models, achieving advanced performance in all assessment tasks, often using only 10% of the labeled data [11]. - The model demonstrated a 10.2% higher performance in early melanoma detection compared to clinical doctors, an 11% improvement in skin cancer diagnosis accuracy for clinical image analysis, and a 16.5% increase in diagnostic accuracy for 128 skin conditions among non-dermatology medical personnel [12][13].