Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance
Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment. However, commonly used report-derived labels for pathology classification or generic image quality metrics for reconstruction may not reliably reflect clinical judgment. We systematically investigate how evaluation-reference choices affect model performance and ranking in both pathology classification and image quality assessment (IQA). To enable controll
Record details
Published: 28 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Healthcare · Finance, VC & PE
Retrieved: 30 July 2026
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ethics.ai (28 July 2026), “Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance,” evidence record 14854, https://ethics.ai/record/14854 (originally published by arXiv cs.LG).
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