This paper is in the following e-collection/theme issue:

Non-randomized Protocols and Methods (ehealth) (572) Skin Cancer and Melanoma Prevention (113) Machine Learning (1501) Artificial Intelligence (1421) Imaging Informatics (200) Image Recognition and AI in Dermatology (12) Machine Learning from Digital Images in Dermatology (10)

Published on in Vol 11, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34896, first published .
Leveraging Artificial Intelligence to Improve the Diversity of Dermatological Skin Color Pathology: Protocol for an Algorithm Development and Validation Study

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

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

Authors of this article:

Eman Rezk1 Author Orcid Image ;   Mohamed Eltorki2 Author Orcid Image ;   Wael El-Dakhakhni1 Author Orcid Image
Article Authors Cited by (19) Tweetations (8) Metrics

Eman Rezk   1 * , MSc ;   Mohamed Eltorki   2 * , MBChB ;   Wael El-Dakhakhni   1 * , PhD

1 School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada

2 Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

*all authors contributed equally

Corresponding Author:

  • Eman Rezk, MSc
  • School of Computational Science and Engineering
  • McMaster University
  • 1280 Main St W
  • Hamilton, ON, L8S 4L8
  • Canada
  • Phone: 1 9055259140
  • Email: rezke@mcmaster.ca