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

TL;DR

This paper emphasizes that medical machine learning models must be designed to be interpretable, shareable, reproducible, and accountable to ensure trust, regulatory approval, and effective clinical application.

Contribution

It advocates for foundational design principles in medical AI, highlighting intrinsically interpretable models, accountability measures, and collaborative, privacy-preserving data sharing methods.

Findings

Intrinsically interpretable models can replace black box approaches in medicine.

Accountability through rigorous evaluation and fairness improves clinical trust.

Federated learning and generative AI enhance reproducibility and data sharing.

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.