• T J Brinker, G Schlager, L E French, T Jutzi, and H Kittler.
• Nachwuchsgruppe Digitale Biomarker für die Onkologie (DBO), Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland. titus.brinker@nct-heidelberg.de.
• Hautarzt. 2020 Sep 1; 71 (9): 669-676.

BackgroundArtificial intelligence (AI) is increasingly being used in medical practice. Especially in the image-based diagnosis of skin cancer, AI shows great potential. However, there is a significant discrepancy between expectations and true relevance of AI in current dermatological practice.ObjectivesThis article summarizes promising study results of skin cancer diagnosis by computer-based diagnostic systems and discusses their significance for daily practice. We hereby focus on the analysis of dermoscopic images of pigmented and unpigmented skin lesions.Materials And MethodsA selective literature search for recent relevant trials was conducted. The included studies used machine learning, and in particular "convolutional neural networks", which have been shown to be particularly effective for the classification of image data.Results And ConclusionsIn numerous studies, computer algorithms were able to detect pigmented and nonpigmented neoplasms of the skin with high precision, comparable to that of dermatologists. The combination of the physician's assessment and AI showed the best results. Computer-based diagnostic systems are widely accepted among patients and physicians. However, they are still not applicable in daily practice, since computer-based diagnostic systems have only been tested in an experimental environment. In addition, many digital diagnostic criteria that help AI to classify skin lesions remain unclear. This lack of transparency still needs to be addressed. Moreover, clinical studies on the use of AI-based assistance systems are needed in order to prove its applicability in daily dermatologic practice.

21 keywords...

hide…