{"ID":2822575,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.02106","arxiv_id":"2601.02106","title":"Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI","abstract":"Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both understandable and verifiable for all stakeholders in the healthcare sector. We present a demonstration of how prototype-based learning can address these needs. Our proposed framework, ProtoPal, features both front- and back-end modes; it achieves superior quantitative performance while also providing an intuitive presentation of interventions and their simulated outcomes.","short_abstract":"Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both understandable and verifiable for all stakeholders in the healthcare sector. We present a demonstration of how prototype-based learning can...","url_abs":"https://arxiv.org/abs/2601.02106","url_pdf":"https://arxiv.org/pdf/2601.02106v1","authors":"[\"Ashish Rana\",\"Ammar Shaker\",\"Sascha Saralajew\",\"Takashi Suzuki\",\"Kosuke Yasuda\",\"Shintaro Kato\",\"Toshikazu Wada\",\"Toshiyuki Fujikawa\",\"Toru Kikutsuji\"]","published":"2026-01-05T13:34:01Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}