A Real-Time Mobile AI-Assisted System for Skin Disease Prescreening: A Technical Feasibility Study

Skin diseases remain a widespread health concern, particularly in areas where access to dermatological care is limited. With the growing availability of smartphones and advances in deep learning, mobile-based image analysis offers a practical option for early-stage screening. This study aims to develop and evaluate a lightweight, real-time mobile AI-assisted system for skin disease prescreening on Android devices.

An Android application, SkinLearn, was developed by integrating a YOLO11-based image classification model optimized for on-device inference using TensorFlow Lite. The model was trained on a publicly available seven-class skin disease dataset and further evaluated on the PAD-UFES-20 skin cancer dataset to examine its ability to generalize across datasets. A YOLOv8 model was also implemented under the same conditions for comparison.

The proposed YOLO11 model achieved a top-1 accuracy of 99.2% on the seven-class dataset and 71.4% on the PAD-UFES-20 dataset, outperforming the YOLOv8 baseline in both cases. The mobile implementation enabled real-time, offline inference with low computational overhead, demonstrating efficient deployment in resource-constrained environments. However, the observed performance drop across datasets highlights challenges related to domain shift, class imbalance, and variability in imaging conditions.

These findings support the technical feasibility of lightweight deep learning models such as YOLO11 for accessible mobile skin disease prescreening. However, the observed performance drop on PAD-UFES-20 indicates that further validation across more diverse datasets and clinical settings is needed before any real-world clinical use is considered.