DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile

• URL: http://arxiv.org/abs/2511.10367v2
• Date: Fri, 14 Nov 2025 19:31:51 GMT
• Title: DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile
• Authors: Thales Bezerra, Emanoel Thyago, Kelvin Cunha, Rodrigo Abreu, Fábio Papais, Francisco Mauro, Natália Lopes, Érico Medeiros, Jéssica Guido, Shirley Cruz, Paulo Borba, Tsang Ing Ren,
• Abstract summary: We introduce DermAI, a lightweight, smartphone-based application that enables real-time capture, annotation, and classification of skin lesions.<n>The DermAI clinical dataset, encompasses a wide range of skin tones, ethinicity and source devices.<n>In preliminary experiments, models trained on public datasets failed to generalize to our samples, while fine-tuning with local data improved performance.
• Score: 1.3591661545452098
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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