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Ensuring that biomedical AI benefits diverse populations
Open accessAbstract
Artificial Intelligence (AI) can potentially impact many aspects of human health, from basic research discovery to individual health assessment. It is critical that these advances in technology broadly benefit diverse populations from around the world. This can be challenging because AI algorithms are often developed on non-representative samples and evaluated based on narrow metrics. Here we outline key challenges to biomedical AI in outcome design, data collection and technology evaluation, and use examples from precision health to illustrate how bias and health disparity may arise in each stage. We then suggest both short term approaches-more diverse data collection and AI monitoring-and longer term structural changes in funding, publications, and education to address these challenges.
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“However, diversity of biomedical datasets is paramount as it constitutes the first step to ensure generalizability to the broader population 145”
From Multimodal biomedical AI (2022) · cites this paper as [145]
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Cites
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“After experiments testing how label choice affected both predictive performance and racial bias, the team recommended using an index variable that combined health prediction with cost prediction [22].”
Method(2018) · Cited by 3,510
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmms2004740 · Cited by 1,731
Ensuring fairness in machine learning to advance health equity.
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018) · Annals of Internal Medicine · doi:10.7326/m18-1990 · Cited by 1,341
Racial bias in pulse oximetry measurement
Sjoding, M. W., Dickson, R. P., Iwashyna, T. J., Gay, S. E., & Valley, T. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2029240 · Cited by 1,114
Genetic analyses of diverse populations improves discovery for complex traits
Wojcik, G. L., Graff, M., Nishimura, K. K., Tao, R., Haessler, J., Gignoux, C. R., Highland, H. M., Patel, Y. M., Sorokin, E. P., Avery, C. L., et al. (2019) · Nature · doi:10.1038/s41586-019-1310-4 · Cited by 1,033
Gupta, V. K., Paul, S., & Dutta, C. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01162 · Cited by 1,019
Churchwell, K., Elkind, M. S. V., Benjamin, R. M., Carson, A. P., Chang, E. K., Lawrence, W., Mills, A., Odom, T. M., Rodriguez, C. J., Rodriguez, F., et al. (2020) · Circulation · doi:10.1161/cir.0000000000000936 · Cited by 984
Bad Blood: The Tuskegee Syphilis Experiment
Kiple, K. F., & Jones, J. H. (1982) · The American Historical Review · doi:10.2307/1870318 · Cited by 817
OECD (2019) · OECD health policy studies · doi:10.1787/3c8385d0-en · Cited by 801
Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919012117 · Cited by 685
Ethical Machine Learning in Healthcare
Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-114757 · Cited by 645
Sex and gender analysis improves science and engineering
Tannenbaum, C., Ellis, R. P., Eyssel, F., Zou, J., & Schiebinger, L. L. (2019) · Nature · doi:10.1038/s41586-019-1657-6 · Cited by 625
Self-citationKrause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.01.034 · Cited by 564
Health at a Glance: Europe 2020
OECD, & Union, E. (2020) · Health at a glance. Europe · doi:10.1787/82129230-en · Cited by 505
The mind has no sex?: women in the origins of modern science
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3353 · Cited by 496
A Longitudinal Big Data Approach for Precision Health
Schüssler-Fiorenza Rose, S. M., Contrepois, K., Moneghetti, K. J., Zhou, W., Mishra, T., Mataraso, S., Dagan-Rosenfeld, O., Ganz, A. B., Dunn, J., Hornburg, D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0414-6 · Cited by 494
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
Self-citationGlobal evolution of research in artificial intelligence in health and medicine: a bibliometric study
Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q.-H., Ho, M.-T., Vuong, T.-T., La, V.-P., Ho, M.-T., Nghiem, K.-C. P., Nguyen, H. L. T., et al. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8030360 · Cited by 472
The Mind Has No Sex? Women in the Origins of Modern Science.
Phanichkul, E. G., & Schiebinger, L. L. (1992) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2076322 · Cited by 429
Self-citationPersonal aging markers and ageotypes revealed by deep longitudinal profiling
Ahadi, S., Zhou, W., Schüssler-Fiorenza Rose, S. M., Sailani, M. R., Contrepois, K., Avina, M., Ashland, M., Brunet, A., & Snyder, M. (2020) · Nature Medicine · doi:10.1038/s41591-019-0719-5 · Cited by 408
Feiner, J. R., Severinghaus, J. W., & Bickler, P. E. (2007) · Anesthesia & Analgesia · doi:10.1213/01.ane.0000285988.35174.d9 · Cited by 400
Assessing risk, automating racism
Benjamin, R. (2019) · Science · doi:10.1126/science.aaz3873 · Cited by 378
The Role of Microbiome in Insomnia, Circadian Disturbance and Depression
Li, Y., Hao, Y., Fan, F., & Zhang, B. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00669 · Cited by 296
Illuminating the dark spaces of healthcare with ambient intelligence
Haque, A., Milstein, A., & Fei-Fei, L. (2020) · Nature · doi:10.1038/s41586-020-2669-y · Cited by 274
Making gender diversity work for scientific discovery and innovation
Nielsen, M. W., Bloch, C. W., & Schiebinger, L. L. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0433-1 · Cited by 260
Self-citationGrosz, B. J., Grant, D. G., Vredenburgh, K., Behrends, J., Hu, L. J., Simmons, A., & Waldo, J. (2019) · Communications of the ACM · doi:10.1145/3330794 · Cited by 249
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
(2018) · doi:10.1145/3278721 · Cited by 208
Bad Blood: The Tuskegee Syphilis Experiment
Challenor, B., & Jones, J. H. (1982) · Journal of Public Health Policy · doi:10.2307/3342040 · Cited by 199
Latent bias and the implementation of artificial intelligence in medicine
DeCamp, M., & Lindvall, C. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa094 · Cited by 198
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
Kim, M. P., Ghorbani, A., & Zou, J. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314287 · Cited by 193
Self-citationUnder‐representation of skin of colour in dermatology images: not just an educational issue
Lester, J., Taylor, S. C., & Chren, M. (2019) · British Journal of Dermatology · doi:10.1111/bjd.17608 · Cited by 188
History of blood gas analysis. VII. Pulse oximetry
Severinghaus, J. W., & Honda, Y. (1987) · The Journal of Clinical Monitoring · doi:10.1007/bf00858362 · Cited by 180
The ethical questions that haunt facial-recognition research
Noorden, R. V. (2020) · Nature · doi:10.1038/d41586-020-03187-3 · Cited by 178
The Effect of Nail Polish on Pulse Oximetry
COT, C. J., Goldstein, E. A., Fuchsman, W. H., & Hoaglin, D. C. (1988) · Anesthesia & Analgesia · doi:10.1213/00000539-198807000-00013 · Cited by 171
An embedded ethics approach for AI development
McLennan, S., Fiske, A., Celi, L. A., Müller, R., Harder, J., Ritt, K., Haddadin, S., & Buyx, A. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0214-1 · Cited by 150
Ries, A. L., Prewitt, L. M., & Johnson, J. J. (1989) · Chest · doi:10.1378/chest.96.2.287 · Cited by 145
Limiting racial disparities and bias for wearable devices in health science research
Colvonen, P. J., DeYoung, P. N., Bosompra, N.-O. A., & Owens, R. L. (2020) · Sleep · doi:10.1093/sleep/zsaa159 · Cited by 137
Editorial policies for sex and gender analysis
Schiebinger, L. L., Leopold, S. S., & Miller, V. M. (2016) · The Lancet · doi:10.1016/s0140-6736(16)32392-3 · Cited by 118
Self-citationThe Gut Microbiome and Individual-Specific Responses to Diet
Leshem, A., Segal, E., & Elinav, E. (2020) · mSystems · doi:10.1128/msystems.00665-20 · Cited by 109
Absence of images of skin of colour in publications of COVID‐19 skin manifestations
Lester, J., Jia, J. L., Zhang, L., Okoye, G. A., & Linos, E. (2020) · British Journal of Dermatology · doi:10.1111/bjd.19258 · Cited by 99
Article Commentary: Sex- and Gender-Based Medicine: The Need for Precise Terminology
Madsen, T. E., Bourjeily, G. R., Hasnain, M., Jenkins, M., Morrison, M. F., Sandberg, K. L., Tong, I. L., Trott, J. A., Werbinski, J. L., & McGregor, A. J. (2017) · Gender and the Genome · doi:10.1089/gg.2017.0005 · Cited by 79
Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19
Röösli, E., Rice, B., & Hernandez-Boussard, T. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa210 · Cited by 76
Ferryman, K. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa133 · Cited by 55
The battle for ethical AI at the world’s biggest machine-learning conference
Gibney, E. (2020) · Nature · doi:10.1038/d41586-020-00160-y · Cited by 54
Achieving fairness in medical devices
Kadambi, A. (2021) · Science · doi:10.1126/science.abe9195 · Cited by 51
Multiaccuracy: Black-Box Post-Processing for Fairness in ClassificationPreprint
Kim, M. P., Ghorbani, A., & Zou, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12317 · Cited by 49
Self-citationHow cancer crossed the color line
(2011) · Choice Reviews Online · doi:10.5860/choice.49-1510 · Cited by 48
Medicalizing Blackness: Making Racial Difference in the Atlantic World
Klepp, S. E. (2018) · Journal of American History · doi:10.1093/jahist/jay317 · Cited by 47
Medicalizing Blackness: Making Racial Difference in the Atlantic World, 1780-1840
Hogarth, R. (2017) · Project Muse (Johns Hopkins University) · Cited by 45
(2018) · doi:10.15620/cdc:82212 · Cited by 40
Hecht, B. J., Wilcox, L. G., Bigham, J. P., Schöning, J., Hoque, M. E., Ernst, J. B., Bisk, Y., Russis, L. D., Yarosh, S., Anjum, B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.09544 · Cited by 37
Diagnostic room-air pulse oximetry: effects of smoking, race, and sex
Witting, M. D., & Scharf, S. M. (2008) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2007.04.002 · Cited by 33
Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations
Fernández‐Rhodes, L., Young, K. L., Lilly, A. G., Raffield, L. M., Highland, H. M., Wojcik, G. L., Agler, C. S., Love, S.-A. M., Okello, S., Petty, L. E., et al. (2020) · Circulation Research · doi:10.1161/circresaha.120.315893 · Cited by 33
McLennan, S., Lee, M. M., Fiske, A., & Celi, L. A. (2020) · The American Journal of Bioethics · doi:10.1080/15265161.2020.1819470 · Cited by 21
Duffy, K. A., Ziolek, T. A., & Epperson, C. N. (2020) · Journal of Women s Health · doi:10.1089/jwh.2019.8084 · Cited by 18
How to Learn when Data Reacts to Your Model: Performative Gradient DescentPreprint
Izzo, Z., Ying, L., & Zou, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07698 · Cited by 15
Self-citationEthical Machine Learning in HealthPreprint
Chen, I. Y., Pierson, E., Rose, S., Joshi, S. D., Ferryman, K., & Ghassemi, M. (2020) · arXiv (Cornell University) · Cited by 10
Choi, B., Kang, B. J., Yun, H., Jeon, B., Bang, J., & Noh, G. (2020) · Anesthesia and Pain Medicine · doi:10.17085/apm.20028 · Cited by 8
Research works printed but not linked: 6
- Moran-Thomas, 2020, How a popular medical device encodes racial bias, Boston Review, 157
- American Medical Assocation, Aubmented Intelligence in Health Care H-480.940. https://policysearch.ama-assn.org/policyfinder/detail/augmented%20intelligence?uri=%2FAMADoc%2FHOD.xml-H-480.940.xml. Accessed 16 March 2021.
- Both authors participated in this process. A review is in progress.
- NeurIPS, Thirty-fourth conference on neural information processing systems, Call for papers, 2020 https://nips.cc/Conferences/2020/CallForPapers. Accessed 23 October 2020.
- The Lancet. Advancing racial equality. 2020. https://www.thelancet.com/racial-equality. Accessed 16 Mar 2021.
- 2020, Technology can't fix this, Nature Mach Intell, 363
Cited by
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Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
The value of standards for health datasets in artificial intelligence-based applications
Arora, A., Alderman, J. E., Palmer, J., Ganapathi, S., Laws, E., McCradden, M. D., Oakden‐Rayner, L., Pfohl, S. R., Ghassemi, M., McKay, F., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02608-w · Cited by 341
Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers
Solanki, P., Grundy, J., & Hussain, W. (2023) · AI and Ethics · doi:10.1007/s43681-022-00195-z · Cited by 157
AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis
Alum, E. U. (2025) · Discover Oncology · doi:10.1007/s12672-025-02064-7 · Cited by 159
Amini, M. M., Jesus, M., Sheikholeslami, D. F., Alves, P. J. P., Benam, A. H., & Hariri, F. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5030053 · Cited by 152
Metadata
- Authors
- Zou, J., & Schiebinger, L.
- Affiliations
- Stanford University (US)
- Corresponding author
- Schiebinger, Londa
- Venue
- EBioMedicine
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 67
- Pages
- 103358
- ISSN
- 2352-3964 (ISSN-L)
- PMC
- PMC8176083
- OpenAlex
- W3158393586
- Cited by
- 124
- References
- 60
- Citations by year
- 2026: 16 · 2025: 43 · 2024: 23 · 2023: 17 · 2022: 22 · 2021: 3
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EBioMedicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1942926)
- National Institutes of Health (P30AG059307, U01MH098953)
- National Science Foundation Directorate for Engineering
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- algorithmic bias, health disparities, underrepresented groups, precision health, data collection, AI evaluation metrics, AI monitoring, research funding
- MeSH terms
- Artificial Intelligence, Health Policy, Humans, Medical Informatics, Health Status Disparities
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