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 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
01.08.2020
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| ISSN: | 1523-1747, 1523-1747 |
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| Abstract | 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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| AbstractList | 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.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. 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. |
| Author | Pfau, Jacob Wei, Maria L Xiong, Mulin Keiser, Michael J Young, Albert T |
| Author_xml | – sequence: 1 givenname: Albert T surname: Young fullname: Young, Albert T organization: Department of Dermatology, University of California, San Francisco, San Francisco, California, USA; Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, California, USA – sequence: 2 givenname: Mulin surname: Xiong fullname: Xiong, Mulin organization: Michigan State University College of Human Medicine, East Lansing, Michigan, USA – sequence: 3 givenname: Jacob surname: Pfau fullname: Pfau, Jacob organization: Department of Dermatology, University of California, San Francisco, San Francisco, California, USA; Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, California, USA – sequence: 4 givenname: Michael J surname: Keiser fullname: Keiser, Michael J organization: Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Institute for Neurodegenerative Diseases, and Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California, USA – sequence: 5 givenname: Maria L surname: Wei fullname: Wei, Maria L email: maria.wei@ucsf.edu organization: Department of Dermatology, University of California, San Francisco, San Francisco, California, USA; Dermatology Service, San Francisco Veterans Affairs Medical Center, San Francisco, California, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA. Electronic address: maria.wei@ucsf.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32229141$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
| Title | Artificial Intelligence in Dermatology: A Primer |
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