Artificial Intelligence in Dermatology: A Primer

Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical validation is currently lacking. We review dermatologic...

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Vydané v:Journal of investigative dermatology Ročník 140; číslo 8; s. 1504
Hlavní autori: Young, Albert T, Xiong, Mulin, Pfau, Jacob, Keiser, Michael J, Wei, Maria L
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.08.2020
Predmet:
Deep Learning - ethics
Dermatology - ethics
Dermatology - methods
Humans
Image Processing, Computer-Assisted - ethics
Image Processing, Computer-Assisted - methods
Referral and Consultation
Skin - diagnostic imaging
Skin - pathology
Skin Diseases - diagnosis
Skin Diseases - pathology
Telemedicine - ethics
Telemedicine - methods
Triage - ethics
Triage - methods
ISSN:1523-1747, 1523-1747
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Popis
Shrnutí:Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical validation is currently lacking. We review dermatological applications of deep learning, the leading artificial intelligence technology for image analysis, and discuss its current capabilities, potential failure modes, and challenges surrounding performance assessment and interpretability. We address the following three primary applications: (i) teledermatology, including triage for referral to dermatologists; (ii) augmenting clinical assessment during face-to-face visits; and (iii) dermatopathology. We discuss equity and ethical issues related to future clinical adoption and recommend specific standardization of metrics for reporting model performance.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ISSN:1523-1747
1523-1747
DOI:10.1016/j.jid.2020.02.026