Literature DB >> 32434009

Deep learning for dermatologists: Part I. Fundamental concepts.

Dennis H Murphree1, Pranav Puri2, Huma Shamim3, Spencer A Bezalel3, Lisa A Drage3, Michael Wang4, Mark R Pittelkow5, Rickey E Carter6, Mark D P Davis3, Alina G Bridges7, Aaron R Mangold5, James A Yiannias8, Megha M Tollefson3, Julia S Lehman7, Alexander Meves3, Clark C Otley3, Olayemi Sokumbi9, Matthew R Hall10, Nneka Comfere7.   

Abstract

Artificial intelligence is generating substantial interest in the field of medicine. One form of artificial intelligence, deep learning, has led to rapid advances in automated image analysis. In 2017, an algorithm demonstrated the ability to diagnose certain skin cancers from clinical photographs with the accuracy of an expert dermatologist. Subsequently, deep learning has been applied to a range of dermatology applications. Although experts will never be replaced by artificial intelligence, it will certainly affect the specialty of dermatology. In this first article of a 2-part series, the basic concepts of deep learning will be reviewed with the goal of laying the groundwork for effective communication between clinicians and technical colleagues. In part 2 of the series, the clinical applications of deep learning in dermatology will be reviewed and limitations and opportunities will be considered.
Copyright © 2020 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  artificial intelligence; deep learning; dermatology; machine learning

Year:  2020        PMID: 32434009      PMCID: PMC7669702          DOI: 10.1016/j.jaad.2020.05.056

Source DB:  PubMed          Journal:  J Am Acad Dermatol        ISSN: 0190-9622            Impact factor:   11.527


  4 in total

1.  Identification of metastatic primary cutaneous squamous cell carcinoma utilizing artificial intelligence analysis of whole slide images.

Authors:  Jaakko S Knuutila; Pilvi Riihilä; Antti Karlsson; Mikko Tukiainen; Lauri Talve; Liisa Nissinen; Veli-Matti Kähäri
Journal:  Sci Rep       Date:  2022-06-14       Impact factor: 4.996

2.  β3 integrin immunohistochemistry as a method to predict sentinel lymph node status in patients with primary cutaneous melanoma.

Authors:  Enrica Quattrocchi; Sindhuja Sominidi-Damodaran; Dennis H Murphree; Alexander Meves
Journal:  Int J Dermatol       Date:  2020-08-09       Impact factor: 2.736

Review 3.  Current state of machine learning for non-melanoma skin cancer.

Authors:  Ajay Nair Sharma; Samantha Shwe; Natasha Atanaskova Mesinkovska
Journal:  Arch Dermatol Res       Date:  2021-05-15       Impact factor: 3.017

Review 4.  Expectations for Artificial Intelligence (AI) in Psychiatry.

Authors:  Scott Monteith; Tasha Glenn; John Geddes; Peter C Whybrow; Eric Achtyes; Michael Bauer
Journal:  Curr Psychiatry Rep       Date:  2022-10-10       Impact factor: 8.081

  4 in total

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