Literature DB >> 32645400

Machine learning for precision dermatology: Advances, opportunities, and outlook.

Ernest Y Lee1, Nolan J Maloney2, Kyle Cheng2, Daniel Q Bach2.   

Abstract

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Year:  2020        PMID： 32645400      PMCID： PMC8023050          DOI： 10.1016/j.jaad.2020.06.1019

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


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  14 in total

1.  Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark.

Authors:  Titus J Brinker; Achim Hekler; Axel Hauschild; Carola Berking; Bastian Schilling; Alexander H Enk; Sebastian Haferkamp; Ante Karoglan; Christof von Kalle; Michael Weichenthal; Elke Sattler; Dirk Schadendorf; Maria R Gaiser; Joachim Klode; Jochen S Utikal
Journal:  Eur J Cancer       Date:  2019-02-22       Impact factor: 9.162

Review 2.  Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma.

Authors:  S M Rajpara; A P Botello; J Townend; A D Ormerod
Journal:  Br J Dermatol       Date:  2009-03-19       Impact factor: 9.302

3.  A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

Authors:  Titus J Brinker; Achim Hekler; Alexander H Enk; Joachim Klode; Axel Hauschild; Carola Berking; Bastian Schilling; Sebastian Haferkamp; Dirk Schadendorf; Stefan Fröhling; Jochen S Utikal; Christof von Kalle
Journal:  Eur J Cancer       Date:  2019-03-08       Impact factor: 9.162

4.  Can clinical decision making be enhanced by artificial intelligence?

Authors:  M Janda; H P Soyer
Journal:  Br J Dermatol       Date:  2019-02       Impact factor: 9.302

Review 5.  Machine Learning in Medicine.

Authors:  Alvin Rajkomar; Jeffrey Dean; Isaac Kohane
Journal:  N Engl J Med       Date:  2019-04-04       Impact factor: 91.245

6.  Embracing machine learning and digital health technology for precision dermatology.

Authors:  Shannon Wongvibulsin; Byron Kalm-Tsun Ho; Shawn G Kwatra
Journal:  J Dermatolog Treat       Date:  2019-06-14       Impact factor: 3.359

7.  A Functional Genomic Meta-Analysis of Clinical Trials in Systemic Sclerosis: Toward Precision Medicine and Combination Therapy.

Authors:  Jaclyn N Taroni; Viktor Martyanov; J Matthew Mahoney; Michael L Whitfield
Journal:  J Invest Dermatol       Date:  2016-12-21       Impact factor: 8.551

Review 8.  Circulating biomarkers predictive of tumor response to cancer immunotherapy.

Authors:  Ernest Y Lee; Rajan P Kulkarni
Journal:  Expert Rev Mol Diagn       Date:  2019-09-10       Impact factor: 5.225

9.  Multimodal skin lesion classification using deep learning.

Authors:  Jordan Yap; William Yolland; Philipp Tschandl
Journal:  Exp Dermatol       Date:  2018-09-27       Impact factor: 3.960

10.  Automated detection of nonmelanoma skin cancer using digital images: a systematic review.

Authors:  Arthur Marka; Joi B Carter; Ermal Toto; Saeed Hassanpour
Journal:  BMC Med Imaging       Date:  2019-02-28       Impact factor: 1.930
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  3 in total

1.  Frequency of Publication of Dermoscopic Images in Inter-observer Studies: A Systematic Review.

Authors:  Sam Polesie; Oscar Zaar
Journal:  Acta Derm Venereol       Date:  2021-12-17       Impact factor: 3.875

Review 2.  Expanding Personalized, Data-Driven Dermatology: Leveraging Digital Health Technology and Machine Learning to Improve Patient Outcomes.

Authors:  Shannon Wongvibulsin; Tracy M Frech; Mary-Margaret Chren; Eric R Tkaczyk
Journal:  JID Innov       Date:  2022-02-01

3.  Predicting Mohs surgery complexity by applying machine learning to patient demographics and tumor characteristics.

Authors:  Gon Shoham; Ariel Berl; Ofir Shir-Az; Sharon Shabo; Avshalom Shalom
Journal:  Exp Dermatol       Date:  2022-03-03       Impact factor: 4.511
  3 in total

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