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.

Accepted to the Demo Track at the IEEE International Conference on Data Mining (ICDM) 2025, where it received the Best Demo Award