Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI

Ashish Rana; Ammar Shaker; Sascha Saralajew; Takashi Suzuki; Kosuke Yasuda; Shintaro Kato; Toshikazu Wada; Toshiyuki Fujikawa; Toru Kikutsuji

arXiv · 2601.02106

Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI

Ashish Rana·Ammar Shaker·Sascha Saralajew·Takashi Suzuki·Kosuke Yasuda·Shintaro Kato·Toshikazu Wada·Toshiyuki Fujikawa·Toru Kikutsuji

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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.

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Ashish Rana, Ammar Shaker, Sascha Saralajew, Takashi Suzuki, Kosuke Yasuda, Shintaro Kato, Toshikazu Wada, Toshiyuki Fujikawa, Toru Kikutsuji. 2026-01-05. Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI. https://arxiv.org/abs/2601.02106

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