As artificial intelligence (AI) applications are increasingly integrated into dermatology clinical practice, questions arise regarding their use in patients of different age groups. Convolutional neural networks (CNNs) trained on adult skin lesion images may not be directly applicable to pediatric populations. However, there are studies focused on pediatric dermatology, particularly in diagnosing complex conditions such as Spitz nevus, based mainly on histopathology, infantile hemangiomas, varicella, and other pediatric infectious disease outbreaks mainly using epidemiologic trends. Notably, the inclusion of pediatric images in CNN trained on adult data has not negatively impacted performance and has even improved efficacy in pediatric care. For elderly patients, AI algorithms are more likely to be incorporated into healthcare systems to streamline prescription services and appointment scheduling, minimizing inconveniences for older patients. Furthermore, combining teledermatology with AI can enhance communication between care providers and doctors, leading to better patient outcomes. AI can also assist in managing the complex medication regimens of elderly patients by checking for potential drug interactions and preventing harmful outcomes. Age considerations are essential when using image-based data to train CNNs for diagnosing and treating patients, ensuring that these systems are adaptable and effective across different age groups.