This article is a correction of the scoping review entitled "Revolutionizing dermatopathology using AI in skin diagnostics". The review follows the PRISMA-ScR guidelines and evaluates the use of AI models to improve the diagnosis of skin diseases.[2][6] Studies use both private clinical data and public sources such as ISIC and MoleMap.[2] Most AI models require better clinical validation and adherence to ethical and legal standards.[2] AI increases the accuracy of lesion classification, facilitates early detection and reduces the diagnostic burden.[2] Examples include architectures such as SkinGPT-4 and DVFNet for analyzing medical images and pathological stains.[2] Models such as CNN and DNN demonstrate high efficiency in both benign and malignant skin diseases.[2] The review highlights limitations such as the modeling of rare disorders and the need for standardized procedures.[2]