This paper provides a comprehensive overview of the role of artificial intelligence (AI), specifically convolutional neural networks (CNN), in dermatology for early identification and diagnosis of skin cancers. While AI has the potential to outperform physicians in some circumstances, considerable difficulties remain. Confounding factors, image quality, and a lack of diversity in training datasets can all have an impact on AI performance. AI application is also hampered by ethical, legal, and practical barriers, like data privacy issues and underrepresentation of varied patient populations. Clinical restrictions, such as a lack of integration between AI and traditional diagnostic methods, exacerbate its implementation. To overcome these issues, the article advises broadening training datasets to reflect population diversity, merging clinical and dermoscopic pictures, and encouraging joint research between humans and artificial intelligence. Overcoming these constraints could boost AI’s utility in dermatology, eventually increasing skin cancer detection and patient outcomes.