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

Machine Learning from Digital Images in Dermatology (17) Artificial Intelligence (2129) Skin Cancer and Melanoma Prevention (123) Innovations and Technology in Cancer Research (43) Imaging Informatics (302) Digital Photography and Imaging in Dermatology (43) Image Recognition and AI in Dermatology (20)

Published on in Vol 4, No 2 (2021): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31697, first published .
Performance of Artificial Intelligence Imaging Models in Detecting Dermatological Manifestations in Higher Fitzpatrick Skin Color Classifications

Performance of Artificial Intelligence Imaging Models in Detecting Dermatological Manifestations in Higher Fitzpatrick Skin Color Classifications

Performance of Artificial Intelligence Imaging Models in Detecting Dermatological Manifestations in Higher Fitzpatrick Skin Color Classifications

Authors of this article:

Pushkar Aggarwal1 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

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  1. Oloruntoba A, Rodrigues M. Bridging the gap in skin cancer research for Australians with skin of colour. Medical Journal of Australia 2023;218(4):148 View
  2. Oliveira R, Ferreira J, Azevedo L, Almeida I. An Overview of Methods to Characterize Skin Type: Focus on Visual Rating Scales and Self-Report Instruments. Cosmetics 2023;10(1):14 View
  3. Shen Y, Li H, Sun C, Ji H, Zhang D, Hu K, Tang Y, Chen Y, Wei Z, Lv J. Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques. npj Digital Medicine 2024;7(1) View
  4. Oloruntoba A, Ingvar Å, Sashindranath M, Anthony O, Abbott L, Guitera P, Caccetta T, Janda M, Soyer H, Mar V. Examining labelling guidelines for AI‐based software as a medical device: A review and analysis of dermatology mobile applications in Australia. Australasian Journal of Dermatology 2024;65(5):409 View
  5. Bhange M, Kothawade S, Telange D, Padwal V. Emerging therapies and innovations in vitiligo management: a comprehensive review. Journal of Immunoassay and Immunochemistry 2025;46(1):1 View
  6. Parekh P, Oyeleke R, Vishwanath T. The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study. JMIR Dermatology 2024;7:e59839 View
  7. Thakker S, Stirrat T, Paredes-Barbeito A, Apte S, Gulati N, Adalsteinsson J, Lipner S, Laughter M, Adotama P. Bridging the dermatological divide: The ethical deployment of artificial intelligence in low-resource settings. Journal of the American Academy of Dermatology 2025 View
  8. Deng J, Guo E, Zhao H, Venugopal K, Moskalyk M. Development of a Transfer Learning-Based, Multimodal Neural Network for Identifying Malignant Dermatological Lesions From Smartphone Images. Cancer Informatics 2025;24 View
  9. Dowie T. Exploring the Diagnostic Capability of Artificial Intelligence in Dermatology for Darker Skin Tones: A Narrative Review. Cureus 2025 View