Artificial Intelligence in Skin Inflammation and Oncology: Diagnostic Innovations in Dermatology

Skin cancer is a prevalent public health problem worldwide, caused by multiple factors like ultraviolet (UV) radiation, environmental toxins, and lifestyle-related risks. Early diagnosis is the key to enhancing patient outcomes, and recent times have seen tremendous development in artificial intelligence (AI)-aided diagnostic approaches. Conventional machine learning (ML) methods, especially Support Vector Machines (SVMs), have demonstrated robust performance in classifying skin lesions based on hand-designed features. However, deep learning has enabled automated feature extraction and enhanced diagnostic accuracy. Convolutional Neural Networks (CNNs) are increasingly used to classify melanomas based on large annotated dermoscopic images, enabling them to perform at expert levels in detecting and segmenting lesions. To support these image-based models, Long Short-Term Memory (LSTM) networks have been utilized to evaluate sequential clinical information, facilitating longitudinal disease monitoring and decision support. In addition, the convergence of CNN and LSTM architectures is the foundation for sophisticated decision support platforms, such as AI-based clinical chatbots that can interact with patients, triage, and provide initial diagnostic advice. Collectively, these innovations usher in the era of hybrid, multimodal AI platforms that will advance precision dermatology and enable earlier, more efficient interventions.