Deep Learning Approaches for Accurate Diagnosis and Prognosis of Skin Cancer

Skin cancer remains one of the most prevalent malignancies worldwide, and delayed diagnosis often leads to poor clinical outcomes. Recent advances in Artificial Intelligence (AI), particularly deep learning and computer vision, have revolutionized dermatological imaging analysis by enabling rapid and accurate detection of skin lesions. AI-assisted systems have emerged as valuable tools for improving diagnostic precision, prognostic evaluation, and accessibility to dermatological care. This review aims to summarize current applications of AI in the diagnosis and prognosis of skin cancer, highlighting its role in lesion classification, clinical decision-making, teledermatology, and medical education. The review also discusses existing limitations and future perspectives for safe clinical integration. A narrative review of published literature on AI-based approaches for skin cancer management was conducted. Studies focusing on machine learning, deep learning, dermatoscopic image analysis, prognostic prediction models, and telemedicine applications were evaluated. Key areas assessed included diagnostic accuracy, lesion classification, patient monitoring, and ethical or regulatory considerations. AI systems demonstrated high diagnostic performance in differentiating malignant from benign skin lesions and accurately identifying melanoma, basal cell carcinoma, and squamous cell carcinoma. These technologies supported early detection, personalized treatment planning, and prognostic assessment using clinical and imaging parameters. AI-enabled telemedicine platforms improved access to dermatological expertise in underserved regions, while annotated datasets enhanced dermatology training and education. Continuous lesion monitoring reduced diagnostic variability and improved follow-up assessment. However, concerns regarding data privacy, algorithm transparency, dataset bias, and regulatory compliance remain significant challenges. AI-based technologies have substantially advanced skin cancer diagnosis and prognosis by enhancing early detection, diagnostic accuracy, and individualized patient care. Despite existing ethical and validation challenges, continued development and rigorous clinical evaluation of AI tools may significantly improve dermatological practice and patient outcomes in the future.