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[2511.11030] Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types
Summary
This study explores how deep learning algorithms trained on normal chest X-rays can predict patients' health insurance types, revealing hidden social inequalities in medical imaging.
Why It Matters
The findings challenge the perception of medical images as neutral, highlighting that AI can inadvertently reflect and perpetuate social disparities. This research is crucial for developing fairer AI systems in healthcare and understanding the implications of socioeconomic factors in medical diagnostics.
Key Takeaways
- Deep learning models can predict health insurance types from chest X-rays with significant accuracy.
- The study reveals that medical images may encode social inequalities, challenging the assumption of neutrality in medical data.
- The findings suggest a need to interrogate the social implications of AI in healthcare, beyond just dataset balancing.
Computer Science > Computer Vision and Pattern Recognition arXiv:2511.11030 (cs) [Submitted on 14 Nov 2025 (v1), last revised 16 Feb 2026 (this version, v5)] Title:Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types Authors:Chi-Yu Chen, Rawan Abulibdeh, Arash Asgari, Sebastián Andrés Cajas Ordóñez, Leo Anthony Celi, Deirdre Goode, Hassan Hamidi, Laleh Seyyed-Kalantari, Ned McCague, Thomas Sounack, Po-Chih Kuo View a PDF of the paper titled Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types, by Chi-Yu Chen and 10 other authors View PDF HTML (experimental) Abstract: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 sug...
