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arXiv · 2608.10084

When Repository Labels Are Not Image-Level Truth: A Supervision Auditing Framework for Chest Radiograph AI

Yesika Alexandra Agudelo-Londoño·Jhon Wilmer Pino-Román·Brahian Carrera Rodríguez·José Miguel Castañeda-Bedoya·Juan Pablo Gómez-López·Aura C. Puche-Sarmiento·Niharika S. D'Souza·Juan Sebastian Osorio-Valencia·Jon E. Duque-Grajales·Jazmín Ximena Suárez-Revelo·Jorge Mario Vélez-Arango·Gabriel Castrillón
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Abstract

Public chest X-ray repositories are widely used to train medical AI systems, yet their labels are typically extracted from radiology reports rather than verified directly on images. As a result, repository labels are often treated as image-level ground truth without validating whether they reflect what is actually visible in the radiograph. We introduce Repository Supervision Auditing (RSA), a framework that evaluates repository-derived labels against expert image-level annotations before model development. Using cardiomegaly in MIMIC-CXR as a case study, RSA compares repository labels with radiologist-reviewed image annotations, characterizes disagreement sources, and builds a curated cohort for deployment-oriented evaluation. Repository-derived cardiomegaly labels showed near-zero agreement with expert image-level assessment, identifying only 1% of expert-confirmed cases. Most discrepancies resulted from non-mention rather than explicit report negation, with expert-confirmed cardiomegaly identified in nearly half of studies assigned a repository-derived No Finding label. Using the resulting expert-curated cohort, a DenseNet121 model achieved a test ROC-AUC of 0.853. These findings show that repository labels may not reliably represent image-level truth and highlight supervision auditing as a critical step for developing trustworthy medical imaging AI.

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Yesika Alexandra Agudelo-Londoño, Jhon Wilmer Pino-Román, Brahian Carrera Rodríguez, José Miguel Castañeda-Bedoya, Juan Pablo Gómez-López, Aura C. Puche-Sarmiento, Niharika S. D'Souza, Juan Sebastian Osorio-Valencia, Jon E. Duque-Grajales, Jazmín Ximena Suárez-Revelo, Jorge Mario Vélez-Arango, Gabriel Castrillón. 2026-08-10. When Repository Labels Are Not Image-Level Truth: A Supervision Auditing Framework for Chest Radiograph AI. https://arxiv.org/abs/2608.10084

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