Revolutionizing Dermatology: A Comprehensive Survey of AI-Enhanced Early Skin Cancer Diagnosis

Skin cancer is a significant global health concern, with its early detection and diagnosis playing a pivotal role in improving patient health outcomes. In recent years, artificial intelligence (AI) has emerged as a transformative force in the field of dermatology, revolutionizing the way skin cancer is detected and diagnosed. This comprehensive survey paper delves into the realm of AI-enhanced early skin cancer diagnosis, offering a thorough examination of the state-of-the-art techniques, methodologies, and advancements in this critical domain. Our survey begins by providing a comprehensive overview of the different types of skin cancer, emphasizing the importance of early detection in preventing disease progression. It then explores the pivotal role that AI and machine learning algorithms play in automating the detection and classification of skin lesions, making dermatology more accessible and accurate. A critical analysis of various AI-driven approaches, including image-based classification, feature extraction, and deep learning models, is presented to elucidate their strengths and limitations. Furthermore, this survey examines the integration of AI into clinical practice, discussing real-world applications, challenges, and ethical considerations. It explores the potential of AI to assist dermatologists in making faster and more accurate diagnoses, ultimately enhancing patient care. The paper also addresses the need for large, diverse datasets and standardization in the development and validation of AI models for skin cancer diagnosis. In conclusion, “Revolutionizing Dermatology” presents a comprehensive synthesis of the current landscape of AI-enhanced early skin cancer diagnosis, offering insights into its transformative potential, challenges, and future directions. By bridging the gap between dermatology and cutting-edge AI technologies, this survey aims to facilitate informed decision-making among researchers, clinicians, and stakeholders in the pursuit of more effective skin cancer detection and treatment strategies.