Abstract: This compilation showcases developments in skin cancer detection using artificial intelligence (AI). In order to increase lesion classification accuracy while adjusting to various datasets and clinical applications, it places a strong emphasis on CNNs, transfer learning, and hybrid techniques. Important developments include multi-modal systems that combine imaging and information to improve diagnostic accuracy, mobile AI for underserved areas, and ensemble learning for increased sensitivity. Additionally covered are ethical issues like biases and patient privacy. Lightweight models and real-time diagnostic tools increase accessibility and make use possible in settings with limited resources. AI integration in healthcare was pioneered by early research on dermoscopic segmentation and the use of CNNs in dermatology. When taken as a whole, these methods demonstrate how AI is revolutionizing skin cancer management through early diagnosis, risk assessment, and individualized treatment.

Keywords: Bias Mitigation, Patient Data Privacy, Resource-Constrained Environments Clinical Applications of AI, Ensemble Learning, Lesion Classification, Dermo scopic Image Segmentation

| DOI: 10.17148/IARJSET.2025.12144