{"ID":2842305,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10367","arxiv_id":"2511.10367","title":"DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.10367","url_pdf":"https://arxiv.org/pdf/2511.10367v2","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\"]","published":"2025-11-13T14:48:07Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}