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

2026-01-05

arXiv.org

AI Summary

This work proposes ProtoPal, a novel framework that introduces prototype learning into medical AI to address the lack of interpretable and verifiable decision mechanisms in existing personalized preventive healthcare approaches. By co-designing front-end and back-end components, ProtoPal integrates explainable artificial intelligence with intervention simulation techniques to generate intuitive, clinically verifiable intervention recommendations alongside their simulated outcomes. The framework maintains strong quantitative performance while significantly enhancing the transparency and trustworthiness of model-driven decisions for stakeholders such as clinicians and patients.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Calibration & Uncertainty Quantification
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• interpretable AI
• personalized preventive healthcare
• prototype-based learning
• understandable predictions
• verifiable recommendations

Innovation

• prototype-based learning
• interpretable AI
• personalized preventive healthcare
• ProtoPal
• explainable machine learning