Implications of predicting race variables from medical images

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0195-0 · Cited by 1,865 · see citing works

Taking race out of human genetics

Yudell, M., Roberts, D., DeSalle, R., & Tishkoff, S. (2016) · Science · doi:10.1126/science.aac4951 · Cited by 631 · see citing works

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

Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00063-2 · Cited by 611 · see citing works

Advances, challenges and opportunities in creating data for trustworthy AI

Liang, W., Tadesse, G. A., Ho, D. E., Fei-Fei, L., Zaharia, M., Zhang, C., & Zou, J. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00516-1 · Cited by 582 · see citing works

How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals

Wu, E., Wu, K., Daneshjou, R., Ouyang, D., Ho, D. E., & Zou, J. (2021) · Nature Medicine · doi:10.1038/s41591-021-01312-x · Cited by 494 · see citing works

Racial Biology and Medical Misconceptions

Deyrup, A., & Graves, J. L. (2022) · New England Journal of Medicine · doi:10.1056/nejmp2116224 · Cited by 74 · see citing works

Confounders mediate AI prediction of demographics in medical imaging

Duffy, G., Clarke, S. L., Christensen, M., He, B. D., Yuan, N., Cheng, S. C., & Ouyang, D. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00720-8 · Cited by 50 · see citing works

Predicting patient demographics from chest Radiographs with deep learning

Adleberg, J., Wardeh, A., Doo, F. X., Marinelli, B., Cook, T. S., Mendelson, D. S., & Kagen, A. (2022) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2022.06.008 · Cited by 32 · see citing works

AI pitfalls and what not to do: mitigating bias in AI

Gichoya, J. W., Thomas, K., Celi, L. A., Safdar, N., Banerjee, I., Banja, J. D., Seyyed-Kalantari, L., Trivedi, H., & Purkayastha, S. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20230023 · Cited by 277 · see citing works

Deep learning-aided decision support for diagnosis of skin disease across skin tones

Groh, M., Badri, O., Daneshjou, R., Koochek, A., Harris, C., Soenksen, L. R., Doraiswamy, P. M., & Picard, R. (2024) · Nature Medicine · doi:10.1038/s41591-023-02728-3 · Cited by 160 · see citing works

Demographic bias in misdiagnosis by computational pathology models

Vaidya, A. J., Chen, R. J., Williamson, D. F. K., Song, A. H., Jaume, G., Yang, Y., Hartvigsen, T., Dyer, E., Lu, M. Y., Lipková, J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02885-z · Cited by 124 · see citing works

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges

Vrudhula, A., Kwan, A. C., Ouyang, D., & Cheng, S. C. (2024) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.123.015495 · Cited by 85 · see citing works

A scoping review and evidence gap analysis of clinical AI fairness

Liu, M., Ning, Y., Teixayavong, S., Liu, X., Mertens, M., Shang, Y., Li, X., Miao, D., Liao, J., Xu, J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01667-2 · Cited by 75 · see citing works

Transforming the cardiometabolic disease landscape: multimodal AI-powered approaches in prevention and management

Muse, E. D., & Topol, E. J. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.02.002 · Cited by 50 · see citing works

Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor

Ladin, K., Cuddeback, J. K., Duru, O. K., Goel, S., Harvey, W., Park, J. G., Paulus, J. K., Sackey, J., Sharp, R. R., Steyerberg, E. W., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01245-y · Cited by 36 · see citing works

Intersectional analysis for science and technology

Nielsen, M. W., Gissi, E., Heidari, S., Horton, R. C., Nadeau, K. C., Ngila, D., Noble, S., Paik, H. Y., Tadesse, G. A., Zeng, E. Y., et al. (2025) · Nature · doi:10.1038/s41586-025-08774-w · Cited by 29 · see citing works

Generative Artificial Intelligence in Medicine

Shanmugam, D., Agrawal, M., Movva, R., Chen, I. Y., Ghassemi, M., Jacobs, M. L., & Pierson, E. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095332 · Cited by 34 · see citing works

MetaGP: A generative foundation model integrating electronic health records and multimodal imaging for addressing unmet clinical needs

Liu, F., Zhou, H., Wang, K., Yu, Y., Gao, Y., Sun, Z., Liu, S., Sun, S., Zou, Z., Li, Z., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102056 · Cited by 22 · see citing works

Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty

Mickley, J. P., Kaji, E. S., Khosravi, B., Mulford, K. L., Taunton, M. J., & Wyles, C. C. (2024) · Arthroplasty Today · doi:10.1016/j.artd.2024.101396 · Cited by 20 · see citing works

Artificial Intelligence and Machine Learning for Inborn Errors of Immunity: Current State and Future Promise

Martinson, A. K., Chin, A. T., Butte, M. J., & Rider, N. L. (2024) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2024.08.012 · Cited by 20 · see citing works

An intentional approach to managing bias in general purpose embedding models

Weng, W., Sellergen, A., Kiraly, A. P., D’Amour, A., Park, J., Pilgrim, R., Pfohl, S., Lau, C. T., Natarajan, V., Azizi, S., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00227-3 · Cited by 17 · see citing works

Generative Artificial Intelligence in Academic Surgery: Ethical Implications and Transformative Potential

Robinson, J. R., Stey, A. M., Schneider, D. F., Kothari, A. N., Lindeman, B. M., Kaafarani, H. M. A., & Haines, K. (2025) · Journal of Surgical Research · doi:10.1016/j.jss.2024.12.059 · Cited by 18 · see citing works

Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives

Queiroz, D., Carlos, A., Anjos, A., & Berton, L. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3793542 · Cited by 8 · see citing works

AI and the ethics of techno-solutionism in pain management

Buchman, D. Z. (2024) · Pain · doi:10.1097/j.pain.0000000000003389 · Cited by 5 · see citing works

Representations and consequences of race in AI systems

Yi, A., & Turner, B. L. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101831 · Cited by 4 · see citing works

Mitigating Algorithmic Bias in Cancer Site Classification Models

Shivanna, A., Spannaus, A., Tschida, J., Gounley, J. P., Krawczuk, P., & Hanson, H. A. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00250 · Cited by 3 · see citing works

DREAM: A framework for discovering mechanisms underlying AI prediction of protected attributesPreprint

Gadgil, S. U., DeGrave, A. J., Janizek, J. D., Xu, S. H., Nwandu, L., Fonjungo, F., Lee, S., & Daneshjou, R. (2024) · medRxiv · doi:10.1101/2024.04.09.24305289 · Cited by 3 · see citing works

The Role of Technology in Research and Innovation: A Taxonomy of ImplicationsPreprint

Barbosu, S., & Teodoridis, F. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4371451 · Cited by 2 · see citing works

Race in Medicine

Diamond-Hunter, M. A. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197625835.013.0016 · Cited by 1 · see citing works

Mitigating Bias Catastrophic Inheritance in Medical Large Vision-Language Models with Logit Fairness Adjustment

Xue, J., Wang, B., Wang, P., Niu, D., & Wei, B. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356416 · Cited by 1 · see citing works

Robin Hood: A De-identification Method to Preserve Minority Representation for Disparities Research

Brown, J. T., Clayton, E. W., Matheny, M. E., Kantarcıoğlu, M., Vorobeychik, Y., & Malin, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-69651-0_5 · Cited by 1 · see citing works

Déléguer aux algorithmes ?

Mignot, L. (2025) · Socio · doi:10.4000/1431e

Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments

Gosciak, J., Balagopalan, A., Ouyang, D., Koenecke, A., Ghassemi, M., & Ho, D. E. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732123

Predicting Patient Self-reported Race From Skin Histological Images with Deep Learning

Chen, S., Verma, R., Clare, K., Jegminat, J., Alleva, E., Huang, K., Veremis, B. M., Fuchs, T. J., & Campanella, G. S. V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05870-6_11

Implications for Future Research, Policy, and Management

Barbosu, S., & Teodoridis, F. (2025) · Springer eBooks · doi:10.1007/978-3-031-98341-2_7

The Missing Millions: Building Cancer Pathology Artificial Intelligence for Safety Net Populations

Jouzi, Z., & Omar, M. (2026) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2026.100374

Fairness in federated medical imaging: a systematic review through the dual fairness lens

Yu, P., Dong, Z., Dhelim, S., Feng, C.-M., & Kechadi, T. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11632-4

Science after Wittgenstein: Reading Encounters with the Affective Grammar of AI-Mediated Science and Educational Orientations

Chan, H., & Erduran, S. (2026) · Science & Education · doi:10.1007/s11191-026-00770-4

Role and Use of Race in AI/ML Models Related to Health (Preprint)Preprint

Were, M. C., Li, A., Malin, B., Yin, Z., Coco, J., Collins, B. X., Clayton, E. W., Novak, L. L., Hendricks‐Sturrup, R., Oluyomi, A. O., et al. (2025) · doi:10.2196/preprints.73996

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

Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00063-2 · Cited by 611 · see citing works

Preventing Artificial Intelligence in Medical Imaging From Perpetuating Health Care Biases and Disparities

Kocher, M., & Lee, C. I. (2022) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2022.07.021 · Cited by 5 · see citing works

AI2 The Impact of Including Race and Ethnicity in Risk Prediction Models on Racial Bias

Khor, S., Hahn, E. E., Haupt, E. C., Shankaran, V., Clark, S., Rodriguez, P. J., Chen, Y., & Bansal, A. (2021) · Value in Health · doi:10.1016/j.jval.2021.04.007 · Cited by 3 · see citing works

Considerations for using predictive models that include race as an input variable: The case study of lung cancer screening

Stevens, E. R., Caverly, T. J., Butler, J. M., Kukhareva, P., Richardson, S. I., Mann, D. M., & Kawamoto, K. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104525 · Cited by 7 · see citing works