The article "Artificial Intelligence and Skin Cancer" by Wei et al. reviews the current progress and potential of artificial intelligence (AI) in the field of skin cancer screening and diagnosis. AI, particularly deep learning convolutional neural networks (CNNs), has shown promising results in classifying skin lesions with performance comparable to or better than dermatologists. The review highlights the diverse applications of AI, including image and molecular processing, and its potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. It also discusses the challenges and barriers to clinical implementation, such as image quality, algorithmic bias, and the need for robust validation. The authors emphasize the importance of high-quality validated models, open transparency, and active stakeholder engagement. Additionally, the article explores the role of AI in increasing access to dermatological care, particularly in underserved populations, and the potential for human-computer collaboration in dermatology. Finally, it outlines areas of active research, including federated learning, uncertainty estimation, and multimodal learning, which are crucial for advancing the clinical application of AI in dermatology.The article "Artificial Intelligence and Skin Cancer" by Wei et al. reviews the current progress and potential of artificial intelligence (AI) in the field of skin cancer screening and diagnosis. AI, particularly deep learning convolutional neural networks (CNNs), has shown promising results in classifying skin lesions with performance comparable to or better than dermatologists. The review highlights the diverse applications of AI, including image and molecular processing, and its potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. It also discusses the challenges and barriers to clinical implementation, such as image quality, algorithmic bias, and the need for robust validation. The authors emphasize the importance of high-quality validated models, open transparency, and active stakeholder engagement. Additionally, the article explores the role of AI in increasing access to dermatological care, particularly in underserved populations, and the potential for human-computer collaboration in dermatology. Finally, it outlines areas of active research, including federated learning, uncertainty estimation, and multimodal learning, which are crucial for advancing the clinical application of AI in dermatology.