Metric Hub: A metric library and practical selection workflow for use-case-driven data quality assessment in medical AI

Data quality is foundational to trustworthy medical AI: The paper emphasizes that quantifying and assessing data quality is a critical factor for developing trustworthy AI systems in medicine, which is essential for both clinical acceptance and regulatory approval.

A practical "metric library" provides standardized tools for evaluation: Rather than relying on ad-hoc approaches, the authors created a collection of data quality metrics with detailed "metric cards" that explain each metric's definition, when to use it, real examples, common pitfalls, and recommendations—making data quality assessment more systematic and accessible to practitioners.

Different medical use cases require different metrics: The paper provides decision trees and strategies for selecting appropriate metrics based on specific use cases, recognizing that a one-size-fits-all approach to data quality assessment is insufficient for the diverse applications of medical AI (therapy selection, monitoring, treatment, etc.).

Data quality assessment should happen at multiple stages: The framework addresses data quality evaluation for both training and testing datasets, emphasizing that trustworthiness requires ongoing scrutiny throughout the ML development pipeline, not just at the end.

Practical implementation guidance reduces barriers to adoption: By providing concrete examples (demonstrated on the PTB-XL ECG dataset) and detailed metric cards, the paper aims to make responsible data quality practices actionable for medical AI developers, moving beyond theoretical frameworks to real-world application.