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

Image Recognition and AI in Dermatology (20) Machine Learning (2076) Artificial Intelligence (2129) Imaging Informatics (302)

Published on in Vol 5, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35150, first published .
Assessing the Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists: Retrospective Comparative Study

Assessing the Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists: Retrospective Comparative Study

Assessing the Generalizability of Deep Learning Models Trained on Standardized and Nonstandardized Images and Their Performance Against Teledermatologists: Retrospective Comparative Study

Authors of this article:

Ayooluwatomiwa I Oloruntoba1, 2 Author Orcid Image ;   Tine Vestergaard3 Author Orcid Image ;   Toan D Nguyen4 Author Orcid Image ;   Zhen Yu2, 5 Author Orcid Image ;   Maithili Sashindranath1 Author Orcid Image ;   Brigid Betz-Stablein6 Author Orcid Image ;   H Peter Soyer6 Author Orcid Image ;   Zongyuan Ge2, 4, 7, 8 Author Orcid Image ;   Victoria Mar1, 9 Author Orcid Image
Article Authors Cited by (8) Tweetations (1) Metrics

Journals

  1. Fogelberg K, Chamarthi S, Maron R, Niebling J, Brinker T. Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation. New Biotechnology 2023;76:106 View
  2. 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
  3. Oloruntoba A, Asghari‐Jafarabadi M, Sashindranath M, Ingvar Å, Adler N, Vico‐Alonso C, Niklasson L, Caixinha A, Hiscutt E, Holmes Z, Assersen K, Adamson S, Jegathees T, Bertelsen T, Velasco‐Tamariz V, Helkkula T, Kristiansen S, Toholka R, Goh M, Chamberlain A, McCormack C, Vestergaard T, Mehta D, Nguyen T, Ge Z, Soyer H, Mar V. Assessment of image quality on the diagnostic performance of clinicians and deep learning models: Cross‐sectional comparative reader study. Journal of the European Academy of Dermatology and Venereology 2025;39(7):1306 View
  4. Ramos-Briceño D, Flammia-D’Aleo A, Fernández-López G, Carrión-Nessi F, Forero-Peña D. Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax. Scientific Reports 2025;15(1) View
  5. Mehta D, Primiero C, Betz‐Stablein B, Nguyen T, Gal Y, Bowling A, Haskett M, Sashindranath M, Bonnington P, Mar V, Soyer H, Ge Z. Multi‐task AI models in dermatology: Overcoming critical clinical translation challenges for enhanced skin lesion diagnosis. Journal of the European Academy of Dermatology and Venereology 2025;39(12):2121 View
  6. Khalil A, Bakheet J, Atiya D, Jehani R, Abdullah R, Haddad M. Cultivating artificial intelligence (AI) competence and shaping attitudes among psychiatric hospital nurses: A quasi-experimental study. DIGITAL HEALTH 2025;11 View

Books/Policy Documents

  1. Najah Q, Almosilhy N, AlEdani E. Applications of Artificial Intelligence in Common Dermatological Diseases. View

Conference Proceedings

  1. Pal O, Paul D, Hasan E, Mohammad M, Bhuiyan M, Ahammed F. 2023 IEEE International Conference on Contemporary Computing and Communications (InC4). Advanced Convolutional Neural Network Model to Identify Melanoma Skin Cancer View