The field of dermatology is witnessing a significant shift towards AI-assisted screening and diagnosis, with a focus on developing accessible and interpretable models for resource-limited environments. Recent studies have demonstrated the potential of deep learning models, particularly Transformer-based architectures, in classifying skin lesions and assessing skin health from mobile-acquired images. These models have shown superior performance in capturing global contextual features and providing transparency in model predictions. The integration of techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) and LIME has enhanced model interpretability, highlighting clinically relevant regions and aiding in error analysis. Furthermore, the development of systematic solutions for remote skin assessment and currency evaluation has bridged the gap between computer vision and skin care research, enabling AI-driven accessible skin analysis for broader real-world applications. Noteworthy papers include: Toward Accessible Dermatology, which demonstrated the effectiveness of Transformer models in skin lesion classification, and AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images, which proposed a novel Skin-Prior Adaptive Vision Transformer model for skin assessment.