AI-based dermatology adoption remains limited by biased datasets, variable image quality, and limited validation. We introduce DermAI, a lightweight, smartphone-based application that enables real-time capture, annotation, and classification of skin lesions during routine consultations. Unlike prior dermoscopy-focused tools, DermAI performs on-device quality checks, and local model adaptation. The DermAI clinical dataset, encompasses a wide range of skin tones, ethinicity and source devices. In preliminary experiments, models trained on public datasets failed to generalize to our samples, while fine-tuning with local data improved performance. These results highlight the importance of standardized, diverse data collection aligned with healthcare needs and oriented to machine learning development.
@article{arxiv.2511.10367,
title = {DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile},
author = {Thales Bezerra and Emanoel Thyago and Kelvin Cunha and Rodrigo Abreu and Fábio Papais and Francisco Mauro and Natália Lopes and Érico Medeiros and Jéssica Guido and Shirley Cruz and Paulo Borba and Tsang Ing Ren},
journal= {arXiv preprint arXiv:2511.10367},
year = {2025}
}