Basic principles of artificial intelligence in dermatology explained using melanoma

被引:0
作者
Hartmann, Tim [1 ,5 ]
Passauer, Johannes [1 ]
Hartmann, Julien [2 ]
Schmidberger, Laura [1 ]
Kneilling, Manfred [1 ,3 ,4 ]
Volc, Sebastian [1 ]
机构
[1] Univ Hosp Tubingen, Dept Dermatol, Tubingen, Germany
[2] Univ Stuttgart, Stuttgart, Germany
[3] Eberhard Karls Univ Tubingen, Werner Siemens Imaging Ctr, Dept Preclin Imaging & Radiopharm, Tubingen, Germany
[4] Eberhard Karls Univ Tubingen, Cluster Excellence iFIT EXC 2180 Image Guided & Fu, Tubingen, Germany
[5] Univ Tubingen Hosp, Dept Dermatol, Liebermeisterstr 25, D-72076 Tubingen, Germany
来源
JOURNAL DER DEUTSCHEN DERMATOLOGISCHEN GESELLSCHAFT | 2024年 / 22卷 / 03期
关键词
artificial intelligence; artificial neural network; machine learning; Melanoma; teledermatology; CONVOLUTIONAL NEURAL-NETWORKS; SKIN-CANCER; LEVEL CLASSIFICATION; MALIGNANT-MELANOMA; PREVENTION; DIAGNOSIS;
D O I
10.1111/ddg.15322
中图分类号
R75 [皮肤病学与性病学];
学科分类号
100206 ;
摘要
The use of artificial intelligence (AI) continues to establish itself in the most diverse areas of medicine at an increasingly fast pace. Nevertheless, many healthcare professionals lack the basic technical understanding of how this technology works, which severely limits its application in clinical settings and research. Thus, we would like to discuss the functioning and classification of AI using melanoma as an example in this review to build an understanding of the technology behind AI. For this purpose, elaborate illustrations are used that quickly reveal the technology involved. Previous reviews tend to focus on the potential applications of AI, thereby missing the opportunity to develop a deeper understanding of the subject matter that is so important for clinical application. Malignant melanoma has become a significant burden for healthcare systems. If discovered early, a better prognosis can be expected, which is why skin cancer screening has become increasingly popular and is supported by health insurance. The number of experts remains finite, reducing their availability and leading to longer waiting times. Therefore, innovative ideas need to be implemented to provide the necessary care. Thus, machine learning offers the ability to recognize melanomas from images at a level comparable to experienced dermatologists under optimized conditions.
引用
收藏
页码:339 / 347
页数:9
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