AI recognition of patient race in medical imaging: a modelling study

dc.contributor.authorGichoya, Judy Wawira
dc.contributor.authorBanerjee, Imon
dc.contributor.authorBhimireddy, Ananth Reddy
dc.contributor.authorBurns, John L.
dc.contributor.authorCeli, Leo Anthony
dc.contributor.authorChen, Li-Ching
dc.contributor.authorCorrea, Ramon
dc.contributor.authorDullerud, Natalie
dc.contributor.authorGhassemi, Marzyeh
dc.contributor.authorHuang, Shih-Cheng
dc.contributor.authorKuo, Po-Chih
dc.contributor.authorLungren, Matthew P.
dc.contributor.authorPalmer, Lyle J.
dc.contributor.authorPrice, Brandon J.
dc.contributor.authorPurkayastha, Saptarshi
dc.contributor.authorPyrros, Ayis T.
dc.contributor.authorOakden-Rayner, Lauren
dc.contributor.authorOkechukwu, Chima
dc.contributor.authorSeyyed-Kalantari, Laleh
dc.contributor.authorTrivedi, Hari
dc.contributor.authorWang, Ryan
dc.contributor.authorZaiman, Zachary
dc.contributor.authorZhang, Haoran
dc.contributor.departmentBioHealth Informatics, School of Informatics and Computingen_US
dc.date.accessioned2022-10-05T19:05:09Z
dc.date.available2022-10-05T19:05:09Z
dc.date.issued2022-06
dc.description.abstractBackground Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91-0·99], CT chest imaging [0·87-0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study. Interpretation The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging. Funding National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationGichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L. C., ... & Zhang, H. (2022). AI recognition of patient race in medical imaging: a modelling study. The Lancet Digital Health. https://doi.org/10.1016/S2589-7500(22)00063-2en_US
dc.identifier.urihttps://hdl.handle.net/1805/30199
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/S2589-7500(22)00063-2en_US
dc.relation.journalThe Lancet Digital Healthen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourcePublisheren_US
dc.subjectmedical imagingen_US
dc.subjectartificial intelligenceen_US
dc.subjectracial identityen_US
dc.titleAI recognition of patient race in medical imaging: a modelling studyen_US
dc.typeArticleen_US
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