Locally calibrated error rates improve interpretability of AI scores and influence radiologist decision-making

Abstract excerpt

<h4>Introduction</h4> Artificial intelligence (AI) systems in radiology commonly generate case-level numeric scores intended to reflect the likelihood of underlying pathology. However, these scores are often difficult to interpret in clinical practice. We propose a framework for translating AI scores into clinically meaningful, locally calibrated error probabilities by providing the corresponding false discovery...

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• AI in cancer detection
• Artificial Intelligence
• Breast
• Breast Density
• Breast Neoplasms
• Cohort Studies
• Mammography
• Radiomics and Machine Learning in Medical Imaging

Identifiers and source

Literature Corpus work

4b974365-674a-59ba-9245-90a41f80fc03

DOI

10.1101/2025.02.28.25323066