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

Theme Issue: AI and ChatGPT in Dermatology (24) Mobile Apps for Dermatology and Prevention (23) Decision Support for Health Professionals (1539) Artificial Intelligence (2129) mHealth for Screening (296) Machine Learning (2076) mHealth for Diagnosis (81) Computer-Aided Diagnosis (251) Teledermatology (69) Imaging Informatics (302)

Published on in Vol 7 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48811, first published .
Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis

Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis

Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis

Authors of this article:

Shiva Shankar Marri1 Author Orcid Image ;   Warood Albadri1 Author Orcid Image ;   Mohammed Salman Hyder1 Author Orcid Image ;   Ajit B Janagond1 Author Orcid Image ;   Arun C Inamadar1 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Journals

  1. Police P, Danda S, Karri S. Diagnostic accuracy of artificial intelligence dermatology apps compared to clinical evaluation in Indian patients with common skin conditions. International Journal of Research in Dermatology 2025;11(4):284 View
  2. Zbrzezny A, Krzywicki T. Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives. Applied Sciences 2025;15(14):7856 View
  3. Korabelnikov D, Lamotkin A. Artificial intelligence in dermatology: a comparative analysis of computer vision programs based on machine learning models. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology 2025 View

Books/Policy Documents

  1. Borja B, Brioschi M, Brioschi G, OÝoung B, Habibi B. Artificial Intelligence over Infrared Images for Medical Applications. View

Conference Proceedings

  1. Gaurav A, Gupta B, Chui K. ICC 2025 - IEEE International Conference on Communications. Optimizing Skin Cancer Detection in E-Health Systems with Mobilenet and Chaos PSO-Enhanced Random Forest View