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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of investigative dermatology Jg. 140; H. 8; S. 1504
Hauptverfasser: Young, Albert T, Xiong, Mulin, Pfau, Jacob, Keiser, Michael J, Wei, Maria L
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.08.2020
Schlagworte:
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
Online-Zugang:Weitere Angaben
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie: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