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

Ashish Rana, Ammar Shaker, Sascha Saralajew, Takashi Suzuki, Kosuke Yasuda +4 more
1/5/2026
cs.LG

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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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Cite this paper

@article{rana2026prototypebased,
  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},
  year   = {2026},
  eprint = {2601.02106},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.02106v1}
}

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