Mpox, previously known as monkeypox, is an evolving zoonotic infection that raises serious public health issues, especially in low‐income settings. The recent outbreaks have indicated the existence of a need for fast, accurate and scalable diagnostics. The article under review examines how up to date convolutional neural networks may be combined with clinical knowledge in the process of early detection of mpox based on pictures of skin lesions. Among the architectures evaluated, MobileNetV2, VGG16 and GoogLeNet reported accuracy exceeding 95% in experiments conducted on publicly available datasets. While these findings demonstrate technical feasibility, such performance does not constitute evidence of clinical readiness. The reported performance reflects controlled offline validation and may be influenced by sample size, limited demographic diversity, and absence of standardized external evaluation. Future work involves building heterogeneous, labeled data sets and the integration of multimodal data for higher diagnostic consistency. Although deep learning‐based systems show promising experimental accuracy and computational efficiency, their integration into clinical workflows requires prospective validation, multi‐center evaluation, and regulatory assessment before real‐world deployment can be recommended.