The integration of artificial intelligence (AI) into clinical practice, particularly in dermatology, brings considerable promise but is not without challenges. A key factor in the performance of AI systems is the quality of input images; issues such as poor framing, inadequate lighting, low resolution, blurriness, improper distance, distortion, and visual noise can significantly degrade diagnostic reliability. Additionally, bias in training data—such as over-representation of lesions from specific populations or age groups—may limit the generalizability of AI predictions across diverse patient demographics. Overfitting can result in inconsistent performance when applied to new images that differ from the training data. Another significant limitation is the “black-box” nature of many AI models. This lack of transparency can be addressed through the development of explainable AI, including techniques like heatmaps that highlight the specific image features influencing the model’s predictions. Furthermore, AI systems are susceptible to various forms of hallucination, including input-conflicting, context-conflicting, and fact-conflicting errors, which may lead to unreliable outputs. Finally, the perceived authority of AI outputs can disproportionately influence less experienced clinicians.