• L V Maul, N Meienberger, and L Kaufmann.
• Klinik für Dermatologie, Universitätsspital Basel, Basel, Schweiz. laravaleska.maul@usb.ch.
• Hautarzt. 2020 Sep 1; 71 (9): 677-685.

BackgroundIn recent years, many medical specialties with a visual focus have been revolutionized by image analysis algorithms using artificial intelligence (AI). As dermatology belongs to this field, it has the potential to play a pioneering role in the use of AI.ObjectiveThe current use of AI for the diagnosis and follow-up of dermatoses is reviewed and the future potential of these technologies is discussed.Materials And MethodsThis article is based on a selective review of the literature using Embase and MEDLINE and the keywords "psoriasis", "eczema", "dermatoses" and "acne" combined with "artificial intelligence", "machine learning", "deep learning", "neural network", "computer-guided", "supervised machine learning" or "unsupervised machine learning" were searched.ResultsIn comparison to examiner-dependent intra- and interindividually fluctuating scores for the assessment of inflammatory dermatoses (e.g. the Psoriasis Areas Severity Index [PASI] and body surface area [BSA]), AI-based algorithms can potentially offer reproducible, standardized evaluations of these scores. Whereas promising algorithms have already been developed for the diagnosis of psoriasis, there is currently only scarce work on the use of AI in the context of eczema.ConclusionsThe latest developments in this field show the enormous potential of AI-based diagnostics and follow-up of dermatological clinical pictures by means of an autonomous computer-based image analysis. These noninvasive, optical examination methods provide valuable additional information, but dermatological interaction remains indispensable in daily clinical practice.

13 keywords...

hide…