Ziad Obermeyer

Ziad Obermeyer (Arabic: زياد أوبرماير) is a Lebanese American physician and researcher whose work focuses on machine learning, health policy, and clinical decision-making in medicine. He is the Blue Cross of California Distinguished Associate Professor at the UC Berkeley School of Public Health, a Chan Zuckerberg Biohub investigator, and a research associate at the National Bureau of Economic Research.^[1][2][3] He is known for research on racial bias in health care algorithms and the use of artificial intelligence in health care.^[4][5]

Obermeyer was born in Beirut, Lebanon, and raised in Cambridge, Massachusetts.^[6] He earned a Bachelor of Arts degree from Harvard College, and a Master of Philosophy (M.Phil.) in History and Science from the University of Cambridge.^[7][8] He received his Doctor of Medicine (M.D.) from Harvard Medical School in 2008, graduating magna cum laude.^[9]

Before pursuing medicine, Obermeyer worked as a consultant at McKinsey & Company, advising pharmaceutical and global health clients in New Jersey, Geneva, and Tokyo.^[8]

After completing his medical degree, he trained as an emergency physician at Mass General Brigham (MGB) in Boston, Massachusetts.^[10][11] He continued to practice emergency medicine at the Fort Defiance Indian Hospital in Arizona, Navajo Nation.^[12]

Obermeyer served as an Assistant Professor at Harvard Medical School from 2014 to 2020.^[13] In 2020, he joined the University of California, Berkeley as an Associate Professor and the Blue Cross of California Distinguished Professor at the School of Public Health.^[14][15]

In 2019, Obermeyer and economist Sendhil Mullainathan analyzed a widely used commercial algorithm designed to identify high-risk patients for specialized care.^[16] Their research revealed that at the same risk score, Black patients were significantly sicker than white patients; for instance, Black patients had 26.3% more chronic conditions than white patients with the same score. This occurred because the algorithm used healthcare costs as a proxy for health needs, failing to account for systemic barriers that result in lower healthcare spending on Black populations.^[17][18] By switching the prediction variable from cost to actual health markers, the researchers reduced this disparity by 84%.^[19][20]

The research was cited in policy discussions on algorithmic accountability and received coverage in mainstream media.^[21][22][23]

In subsequent research, Obermeyer has continued to investigate bias in healthcare algorithms, including studies demonstrating disparities in clinical risk prediction tools. His work has contributed to policy discussions on regulating artificial intelligence in medicine and improving fairness in automated healthcare decision-making systems.^[24]

In 2021, Obermeyer and Mullainathan published a study, analyzing physician decision-making in cardiac care using machine learning models.^[25] They compared machine learning models and physician judgment in identifying patients at risk for heart attacks, and analyzed their patterns of diagnostic testing.^[26] They found that physicians misdiagnose cases when they rely on symptoms representative of a heart attack, such as chest pain, over others.^[27][28]

In 2020, Obermeyer published a study analyzing an algorithm used to allocate CARE Act relief funding to hospitals.^[29] The study identified allocation patterns that favored hospitals with higher revenues over those serving larger numbers of COVID-19 patients who are predominantly black populations.^[30][31]

The results of the study aligned with research documenting the disparate impact of the COVID-19 pandemic on communities of color in the United States.^[32][33][34]

Obermeyer and Computer Scientist Emma Pierson developed a deep learning approach to investigate unexplained pain disparities and the severity of osteoarthritis in underserved communities.^[35] Their research found that traditional radiographic assessments accounted for 9% of racial disparities in pain, while algorithmic predictions explained 43%, suggesting that machine learning trained on diverse datasets can augment the work of radiologists.^[36]

Following the publication of the 2019 algorithmic racial bias study, the New York Department of Financial Services and Department of Health launched an investigation into UnitedHealth Group's algorithm, requesting that the company cease using it, citing discriminatory business practices.^[37][38]

In December 2019, Democratic Senators Cory Booker and Ron Wyden released letters to the Federal Trade Commission and Centers for Medicare and Medicaid Services asking to investigate potential discrimination in decision-making algorithms against marginalized communities in healthcare.^[39][40] The senators also wrote to major healthcare companies, including Aetna and Blue Cross Blue Shield, about their internal safeguards against racial bias in their technology.^[41]

In 2021, Obermeyer and colleagues at the University of Chicago Booth School of Business released the Algorithmic Bias Playbook, a resource for policymakers and technical teams working in healthcare, on how to measure and mitigate algorithmic racial bias.^[42][43]

Obermeyer testified before the U.S. Senate Financial Committee in February 2024 on artificial intelligence in healthcare, recommending transparency requirements for AI developers and independent algorithm evaluations.^[44] In December 2025, he testified before the United States House Committee on Oversight and Government Reform on the role of AI in affordable healthcare and the impact of its integration on the workforce.^[45]

In 2021, Obermeyer cofounded Nightingale Open Science, a non-profit that creates new medical imaging datasets available for research.^[46]

Obermeyer cofounded Dandelion Health, a health data analytics company.^[47] In June 2023, the company launched a program, funded by the Gordon and Betty Moore Foundation and the SCAN Foundation, to audit and evaluate the performance of algorithms across different populations and geographies to identify potential racial, ethnic, and geographic bias.^[48][49] Dandelion Health partnered with the American Heart Association in 2025 to power an AI assessment lab for cardiovascular algorithms.^[50][51]

Obermeyer is a founding faculty member of the University of California, Berkeley – University of California, San Francisco joint program in Computational Precision Health.^[52]

TIME magazine named Obermeyer one of the 100 most influential people in artificial intelligence in 2023.^[53] He has served as a Chan Zuckerberg Biohub Investigator since 2022 and as a Research Associate at the National Bureau of Economic Research since 2023.^[54][55] He was designated an Emerging Leader by the National Academy of Medicine in 2020.^[56]

Obermeyer's racial bias study received the Willard G. Manning Memorial Award for the Best Research in Health Econometrics from the American Society of Health Economists (ASHEcon) in 2021 and the Responsible Business Education Award from the Financial Times in 2022.^[57][58]

In 2012, he received the Early Independence Award from the National Institutes of Health for his research on predictors of unexpected deaths in the United States.^[59]