Artificial intelligence is revealing what medicine never intended to encode. Deep vision models, trained on chest X-rays, can now detect not only disease but also invisible traces of social inequality. In this study, we show that state-of-the-art architectures (DenseNet121, SwinV2-B, MedMamba) can predict a patient's health insurance type, a strong proxy for socioeconomic status, from normal chest X-rays with significant accuracy (AUC around 0.70 on MIMIC-CXR-JPG, 0.68 on CheXpert). The signal was unlikely contributed by demographic features by our machine learning study combining age, race, and sex labels to predict health insurance types; it also remains detectable when the model is trained exclusively on a single racial group. Patch-based occlusion reveals that the signal is diffuse rather than localized, embedded in the upper and mid-thoracic regions. This suggests that deep networks may be internalizing subtle traces of clinical environments, equipment differences, or care pathways; learning socioeconomic segregation itself. These findings challenge the assumption that medical images are neutral biological data. By uncovering how models perceive and exploit these hidden social signatures, this work reframes fairness in medical AI: the goal is no longer only to balance datasets or adjust thresholds, but to interrogate and disentangle the social fingerprints embedded in clinical data itself.
@article{arxiv.2511.11030,
title = {Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types},
author = {Chi-Yu Chen and Rawan Abulibdeh and Arash Asgari and Sebastián Andrés Cajas Ordóñez and Leo Anthony Celi and Deirdre Goode and Hassan Hamidi and Laleh Seyyed-Kalantari and Ned McCague and Thomas Sounack and Po-Chih Kuo},
journal= {arXiv preprint arXiv:2511.11030},
year = {2026}
}