Obermeyer, Ziad

Dissecting racial bias in an algorithm used to manage the health of populations

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 7,137

Predicting the future - big data, machine learning, and clinical medicine

Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 3,652

Coverage of cervical cancer screening in 57 countries: low average levels and large inequalities

Gakidou, E., Nordhagen, S., & Obermeyer, Z. (2008) · PLoS Medicine

Cervical Cancer and HPV ResearchWomen's cancer prevention and management

Cited by 976

Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.

Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., et al. (2020) · Nature Medicine

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 665

Prediction Policy Problems

Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review

Advanced Causal Inference TechniquesEvaluation and Performance Assessment

Cited by 584

Considerations for addressing bias in artificial intelligence for health equity

Abràmoff, M. D., Tarver, M. E., Loyo-Berrios, N., Trujillo, S., Char, D., Obermeyer, Z., Eydelman, M. B., Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group of the Collaborative Community for Ophthalmic Imaging Foundation, W., & Maisel, W. H. (2023) · npj Digital Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 349

Emergency care in 59 low- and middle-income countries: a systematic review

Obermeyer, Z., Abujaber, S., Makar, M., Stoll, S., Kayden, S. R., Wallis, L. A., Reynolds, T. A., & on behalf of the Acute Care Development Consortium (2015) · Bulletin of the World Health Organization

Emergency and Acute Care StudiesTrauma and Emergency Care Studies

Cited by 319

An algorithmic approach to reducing unexplained pain disparities in underserved populations

Pierson, E., Cutler, D. M., Leskovec, J., Mullainathan, S., & Obermeyer, Z. (2021) · Nature Medicine

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 268

Lost in thought—the limits of the human mind and the future of medicine

Obermeyer, Z., & Lee, T. H. (2017) · New England Journal of Medicine

Artificial Intelligence in Healthcare and EducationTechnology and Human Factors in Education and Health

Cited by 252

Regulation of predictive analytics in medicine

Parikh, R. B., Obermeyer, Z., & Navathe, A. S. (2019) · Science

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 223

Fifty years of violent war deaths from Vietnam to Bosnia: analysis of data from the world health survey programme

Obermeyer, Z., Murray, C. J. L., & Gakidou, E. (2008) · BMJ

Health and Conflict StudiesEnvironmental and Biological Research in Conflict Zones

Cited by 200

Does Machine Learning Automate Moral Hazard and Error?

Mullainathan, S., & Obermeyer, Z. (2017) · American Economic Review

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 156

Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People

Obermeyer, Z., & Mullainathan, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 124

Measuring Adult Mortality Using Sibling Survival: A New Analytical Method and New Results for 44 Countries, 1974–2006

Obermeyer, Z., Rajaratnam, J. K., Park, C. H., Gakidou, E., Hogan, M. C., Lopez, A. D., & Murray, C. J. L. (2010) · PLoS Medicine

Insurance, Mortality, Demography, Risk ManagementGlobal Maternal and Child Health

Cited by 110

The case for algorithmic stewardship for artificial intelligence and machine learning technologies

Eaneff, S., Obermeyer, Z., & Butte, A. J. (2020) · JAMA

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 101

Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review: Scoping review examines racial and ethnic bias in clinical algorithms

Cary, M. P., Zink, A., Wei, S., Olson, A., Yan, M., Senior, R., Bessias, S., Gadhoumi, K., Jean-Pierre, G., Wang, D., et al. (2023) · Health Affairs

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 94

Direct Uncertainty Prediction for Medical Second Opinions

Raghu, M., Blumer, K., Sayres, R., Obermeyer, Z., Kleinberg, R., Mullainathan, S., & Kleinberg, J. (2018) · arXiv

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 93

The Algorithmic Automation Problem: Prediction, Triage, and Human Effort

Raghu, M., Blumer, K., Corrado, G., Kleinberg, J., Obermeyer, Z., & Mullainathan, S. (2019) · arXiv

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 58

Solving medicine's data bottleneck: Nightingale Open Science

Mullainathan, S., & Obermeyer, Z. (2022) · Nature Medicine

Research Data Management PracticesScientific Computing and Data Management

Cited by 43

Allocation of COVID-19 Relief Funding to Disproportionately Black Counties

Kakani, P., Chandra, A., Mullainathan, S., & Obermeyer, Z. (2020) · JAMA

COVID-19 Clinical Research StudiesCOVID-19 epidemiological studies

Cited by 41

Implications of predicting race variables from medical images

Zou, J., Gichoya, J. W., Ho, D. E., & Obermeyer, Z. (2023) · Science

Race, Genetics, and SocietyArtificial Intelligence in Healthcare and Education

Cited by 30