Digitalization in dermato-oncology: artificial intelligence-based diagnostic tools

Cutaneous malignancies are one of the most common dermatological diagnoses and—depending on subtype—are associated with high mortality and morbidity. Initial assessment includes macroscopic and dermoscopic analysis of the lesion, where melanocytic lesions can be especially challenging. Artificial intelligence (AI) is a subtype of machine learning and enables patterns to be learnt on huge, standardized datasets, with subsequent successful application of the algorithm to new data. AI algorithms can also learn patterns which elude human perception. Data in dermato-oncology include not only clinical and dermoscopic images, but also 3D full-body scans, histological slides, and the output of new imaging modalities such as line-field optical coherence tomography, all of which are well suited for AI analysis. While for AI-based assessment of melanocytic lesions on clinical/dermoscopic images there are already market-ready applications, AI research in other domains of dermato-oncology is still in its infancy. Particularly for the analysis of highly standardized data like 3D full-body scans, histological slides, and data from new imaging modalities, the full potential of AI analysis not yet exhausted. AI in dermato-oncology must be used critically and against its technical background. Limitations and obstacles include the need for standardized data and the specificity of AI algorithms for the question they have been trained to answer. © 2023, The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.