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.
@article{arxiv.2601.02106,
title = {Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI},
author = {Ashish Rana and Ammar Shaker and Sascha Saralajew and Takashi Suzuki and Kosuke Yasuda and Shintaro Kato and Toshikazu Wada and Toshiyuki Fujikawa and Toru Kikutsuji},
journal= {arXiv preprint arXiv:2601.02106},
year = {2026}
}
Comments
Accepted to the Demo Track at the IEEE International Conference on Data Mining (ICDM) 2025, where it received the Best Demo Award