期刊
JOURNAL OF INVESTIGATIVE DERMATOLOGY
卷 140, 期 8, 页码 1504-1512出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jid.2020.02.026
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资金
- University of California, San Francisco Helen Diller Family Comprehensive Cancer Center Impact Award
- Melanoma Research Alliance
- National Center for Advancing Translational Sciences, National Institutes of Health [5TL1TR001871-04]
- American Skin Association Hambrick Medical Student Grant Targeting Melanoma and Skin Cancer
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.
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