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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.11030 |
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| _version_ | 1866918447943778304 |
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| author | Chen, Chi-Yu Abulibdeh, Rawan Asgari, Arash Ordóñez, Sebastián Andrés Cajas Celi, Leo Anthony Goode, Deirdre Hamidi, Hassan Seyyed-Kalantari, Laleh McCague, Ned Sounack, Thomas Kuo, Po-Chih |
| author_facet | Chen, Chi-Yu Abulibdeh, Rawan Asgari, Arash Ordóñez, Sebastián Andrés Cajas Celi, Leo Anthony Goode, Deirdre Hamidi, Hassan Seyyed-Kalantari, Laleh McCague, Ned Sounack, Thomas Kuo, Po-Chih |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types Chen, Chi-Yu Abulibdeh, Rawan Asgari, Arash Ordóñez, Sebastián Andrés Cajas Celi, Leo Anthony Goode, Deirdre Hamidi, Hassan Seyyed-Kalantari, Laleh McCague, Ned Sounack, Thomas Kuo, Po-Chih Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2511.11030 |