Artificial Intelligence in Dermatology: Promises, Pitfalls, and Practical Realities

Abstract

Artificial intelligence (AI) is rapidly transforming dermatology, enhancing diagnostic accuracy, clinical efficiency, and patient care. Using machine learning and deep neural networks, particularly convolutional neural networks, AI demonstrates expert-level performance in classifying skin lesions such as melanoma, etc. Beyond image analysis, emerging multimodal approaches integrate clinical, histopathological, and genomic data, enabling improved diagnosis, prognostication, and personalized treatment strategies. AI also supports disease severity assessment, ulcer evaluation, and teledermatology, expanding access to care.

However, challenges remain, including biases from underrepresented skin types, variability in image quality, and the need for regulatory validation and real-world studies. Ethical concerns, such as data privacy and over-reliance on AI, must also be addressed.

Future developments include automated severity scoring, longitudinal lesion monitoring, and integration with advanced technologies like 3D imaging and large language models. Collaborative, dermatologist-led development and robust regulatory frameworks will be essential for safe and effective clinical integration.