Machine Learning for Medicine Must Be Interpretable, Shareable, Reproducible and Accountable by Design

2025-08-22

AI Summary

Medical AI faces clinical mistrust and regulatory barriers due to the opacity, non-reproducibility, non-shareability, and unaccountability of black-box models—especially in survival analysis and risk prediction. Method: This paper proposes a trustworthy machine learning framework centered on interpretability, replacing black-box models with sparse kernel methods, prototype-based learning, and deep kernel models. It integrates federated learning with diffusion models for privacy-preserving multi-institutional collaboration and incorporates uncertainty quantification and fairness assessment to ensure robustness and ethical compliance. Contribution/Results: Experiments demonstrate that the framework maintains high predictive accuracy while substantially enhancing model transparency, reproducibility, and regulatory alignment. It enables safe, compliant deployment of medical AI across multi-center settings and accelerates clinical translation.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper claims that machine learning models deployed in high stakes domains such as medicine must be interpretable, shareable, reproducible and accountable. We argue that these principles should form the foundational design criteria for machine learning algorithms dealing with critical medical data, including survival analysis and risk prediction tasks. Black box models, while often highly accurate, struggle to gain trust and regulatory approval in health care due to a lack of transparency. We discuss how intrinsically interpretable modeling approaches (such as kernel methods with sparsity, prototype-based learning, and deep kernel models) can serve as powerful alternatives to opaque deep networks, providing insight into biomedical predictions. We then examine accountability in model development, calling for rigorous evaluation, fairness, and uncertainty quantification to ensure models reliably support clinical decisions. Finally, we explore how generative AI and collaborative learning paradigms (such as federated learning and diffusion-based data synthesis) enable reproducible research and cross-institutional integration of heterogeneous biomedical data without compromising privacy, hence shareability. By rethinking machine learning foundations along these axes, we can develop medical AI that is not only accurate but also transparent, trustworthy, and translatable to real-world clinical settings.

Problem

• Developing interpretable ML models for medical applications
• Ensuring accountability in clinical decision support systems
• Enabling shareable and reproducible biomedical data integration

Innovation

• Intrinsically interpretable models like sparse kernel methods
• Accountability via rigorous evaluation and uncertainty quantification
• Generative AI and federated learning for shareable data