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

2026-01-30

AI Summary

This study addresses the critical gap in systematic data quality assessment for medical AI, which hinders the development and clinical deployment of trustworthy AI systems. Building upon the METRIC framework, the authors introduce the first comprehensive Metric Hub—a curated repository of data quality metrics tailored for medical AI—along with a structured metric selection protocol and a decision tree to guide context-specific metric choice. Innovatively organizing data quality dimensions through metric cards and adopting a use-case-driven strategy, the approach is validated on the PTB-XL electrocardiogram dataset. The work establishes a standardized, actionable pathway for evaluating whether training and testing data are “fit-for-purpose” in medical AI applications, thereby advancing rigorous and transparent data quality practices in the field.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

Machine learning (ML) in medicine has transitioned from research to concrete applications aimed at supporting several medical purposes like therapy selection, monitoring and treatment. Acceptance and effective adoption by clinicians and patients, as well as regulatory approval, require evidence of trustworthiness. A major factor for the development of trustworthy AI is the quantification of data quality for AI model training and testing. We have recently proposed the METRIC-framework for systematically evaluating the suitability (fit-for-purpose) of data for medical ML for a given task. Here, we operationalize this theoretical framework by introducing a collection of data quality metrics - the metric library - for practically measuring data quality dimensions. For each metric, we provide a metric card with the most important information, including definition, applicability, examples, pitfalls and recommendations, to support the understanding and implementation of these metrics. Furthermore, we discuss strategies and provide decision trees for choosing an appropriate set of data quality metrics from the metric library given specific use cases. We demonstrate the impact of our approach exemplarily on the PTB-XL ECG-dataset. This is a first step to enable fit-for-purpose evaluation of training and test data in practice as the base for establishing trustworthy AI in medicine.

Problem

• data quality
• medical AI
• fit-for-purpose
• metric selection
• trustworthy AI

Innovation

• metric library
• data quality assessment
• fit-for-purpose
• medical AI
• decision tree