Literature DB >> 32229141

Artificial Intelligence in Dermatology: A Primer.

Albert T Young1, Mulin Xiong2, Jacob Pfau1, Michael J Keiser3, Maria L Wei4.   

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.
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

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Year:  2020        PMID: 32229141     DOI: 10.1016/j.jid.2020.02.026

Source DB:  PubMed          Journal:  J Invest Dermatol        ISSN: 0022-202X            Impact factor:   8.551


  11 in total

Review 1.  Applications of Telemedicine in Dermatology.

Authors:  Eshita Sud; Ashish Anjankar
Journal:  Cureus       Date:  2022-08-07

2.  A Workflow for Computer-Aided Evaluation of Keloid Based on Laser Speckle Contrast Imaging and Deep Learning.

Authors:  Shuo Li; He Wang; Yiding Xiao; Mingzi Zhang; Nanze Yu; Ang Zeng; Xiaojun Wang
Journal:  J Pers Med       Date:  2022-06-16

3.  Accuracy of commercially available smartphone applications for the detection of melanoma.

Authors:  M D Sun; J Kentley; P Mehta; S Dusza; A C Halpern; V Rotemberg
Journal:  Br J Dermatol       Date:  2022-01-20       Impact factor: 11.113

4.  Development of High-Quality Artificial Intelligence in Dermatology: Guidelines, Pitfalls, and Potential.

Authors:  Carrie Kovarik
Journal:  JID Innov       Date:  2022-09-07

5.  Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models.

Authors:  Albert T Young; Kristen Fernandez; Jacob Pfau; Rasika Reddy; Nhat Anh Cao; Max Y von Franque; Arjun Johal; Benjamin V Wu; Rachel R Wu; Jennifer Y Chen; Raj P Fadadu; Juan A Vasquez; Andrew Tam; Michael J Keiser; Maria L Wei
Journal:  NPJ Digit Med       Date:  2021-01-21

Review 6.  Deep learning-enabled medical computer vision.

Authors:  Andre Esteva; Katherine Chou; Serena Yeung; Nikhil Naik; Ali Madani; Ali Mottaghi; Yun Liu; Eric Topol; Jeff Dean; Richard Socher
Journal:  NPJ Digit Med       Date:  2021-01-08

7.  Skin lesion classification system using a K-nearest neighbor algorithm.

Authors:  Mustafa Qays Hatem
Journal:  Vis Comput Ind Biomed Art       Date:  2022-03-01

8.  Leveraging Artificial Intelligence to Improve the Diversity of Dermatological Skin Color Pathology: Protocol for an Algorithm Development and Validation Study.

Authors:  Eman Rezk; Mohamed Eltorki; Wael El-Dakhakhni
Journal:  JMIR Res Protoc       Date:  2022-03-08

9.  Dermoscopic Photographs Impact Confidence and Management of Remotely Triaged Skin Lesions.

Authors:  Tova Rogers; Myles Randolph McCrary; Howa Yeung; Loren Krueger; Suephy C Chen
Journal:  Dermatol Pract Concept       Date:  2022-07-01

10.  Automatic wound detection and size estimation using deep learning algorithms.

Authors:  Héctor Carrión; Mohammad Jafari; Michelle Dawn Bagood; Hsin-Ya Yang; Roslyn Rivkah Isseroff; Marcella Gomez
Journal:  PLoS Comput Biol       Date:  2022-03-11       Impact factor: 4.475

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