Authors:
G. Brintha, Amira Sanoli, M. Arline Jemi Flora, Madhan Raj Gopi Akila, S. Tejas
Addresses:
Department of Artificial Intelligence and Data Science, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India. Department of Computer Science, University of Stuttgart, Stuttgart, Baden-Württemberg, Germany. Department of Data Science, Analytics and Engineering, Arizona State University, Tempe, Arizona, United States of America.
Abstract:
The AI-Based Skin Disease Detection and Personalised Care Assistant aims to make skin care easier to access, more accurate, and easier to use for people in both cities and rural areas. The system uses a Flutter-based mobile app and a Python Flask backend to make it easy to handle data and analyse it in real time. The app can analyse pictures of skin problems taken directly with a smartphone camera using deep learning models such as MobileNetV2. The system uses OpenCV techniques to assess the severity of detected conditions, enabling it to distinguish between mild, moderate, and severe cases. The assistant uses this information to make personalised care suggestions, which help users learn more about their condition and decide what to do next. This helps people assess their own health, reduces unnecessary hospital visits, and makes dermatologists' jobs easier. For more serious cases, GPS integration lets the system quickly connect users with dermatology specialists in their area, ensuring they receive medical help right away. There are strong security measures in place to keep private medical information safe and protect patient privacy. Overall, the solution creates a safe, smart, and easy-to-use digital platform. It helps with early detection, supports informed decision-making, and encourages better skin health management worldwide through modern AI technologies.
Keywords: Severity Estimation; GPS Integration; Deep Learning (DL); Skin Disease Detection; Skin Health Management; Modern AI Technologies; Decision-Making; Dermatologists' Jobs.
Received on: 24/02/2025, Revised on: 18/06/2025, Accepted on: 26/09/2025, Published on: 05/01/2026
DOI: 10.64091/ATIHL.2026.000265
AVE Trends in Intelligent Health Letters, 2026 Vol. 3 No. 1 , Pages: 53-67