Deep learning is revolutionizing dermatology, enabling accurate diagnosis of skin lesions, particularly melanoma. Early research demonstrated its potential, but limitations in training data hindered real-world application. Recent advances include diverse datasets and integration with noninvasive imaging techniques, leading to artificial intelligence-powered tools for clinical use. Challenges remain, including the need for robust validation methods, addressing biases, and ensuring patient safety through postmarket surveillance. Foundation models hold promise for future development but require careful consideration of ethical and practical implications. Collaboration between stakeholders is crucial to successfully integrate this transformative technology and improve patient outcomes.