Biography:Ziad Obermeyer

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Ziad Obermeyer
زياد أوبرماير
Error creating thumbnail:
Obermeyer in 2019
CitizenshipAmerican
Alma materHarvard College, University of Cambridge, Harvard Medical School
OccupationPhysician, researcher
EmployerUniversity of California, Berkeley

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 his research on racial bias in health care algorithms and the use of artificial intelligence in health care.[4][5]

Early life and education

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 later continued practicing emergency medicine at the Fort Defiance Indian Hospital on the Navajo Nation[12] in Arizona.

Academic career

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]

Research focus

Algorithmic racial bias in healthcare

In a 2019 study, Obermeyer and economist Sendhil Mullainathan examined a commercial healthcare algorithm developed by the Optum unit of UnitedHealth Group used in hospitals.[16] The study found that the algorithm systematically underestimated the health needs of Black patients compared to white patients with similar conditions.[17][18] The researchers demonstrated that reformulating the algorithm reduced racial bias.[19][20]

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

Clinical decision-making

In 2021, Obermeyer and Mullainathan examined physician decision-making in cardiac care using machine learning models.[24] They compared machine learning models and physician judgment in identifying patients at risk for heart attacks and analyzed their patterns of diagnostic testing.[25] They found that physicians misdiagnose cases when they rely on symptoms representative of a heart attack, such as chest pain, over others.[26][27]

COVID-19 funding allocation

In 2020, Obermeyer published a study analyzing an algorithm used to allocate CARE Act relief funding to hospitals.[28] 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.[29][30]

The findings of the study aligned with research documenting the disparate impact of the COVID-19 pandemic on communities of color in the United States.[31][32][33]

Pain assessment

Obermeyer and Computer Scientist Emma Pierson developed a deep learning approach to investigate unexplained pain disparities and the severity of osteoarthritis in underserved communities.[34] 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.[35]

Policy and regulatory work

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.[36][37]

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.[38][39] 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.[40]

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.[41][42]

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.[43] 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.[44]

Organizations

In 2021, Obermeyer cofounded Nightingale Open Science, a non-profit that creates new medical imaging datasets available for research. and Dandelion Health, a health data analytics company.[45][46] 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 to identify potential racial, ethnic, and geographic bias.[47][48] Dandelion Health partnered with the American Heart Association in 2025 to power an AI assessment lab for cardiovascular algorithms.[49]

Obermeyer is a founding faculty member of the University of California, Berkeley–University of California, San Francisco joint program in computational precision health.[50]

Recognition

TIME magazine named Obermeyer one of the 100 most influential people in artificial intelligence in 2023.[51] 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.[52][53] He was designated an Emerging Leader by the National Academy of Medicine in 2020.[54]

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.[55][56]

References

  1. ↑ "Ziad Obermeyer" (in en-US). 2019-07-16. https://publichealth.berkeley.edu/people/ziad-obermeyer. 
  2. ↑ Svoboda, Dylan (2022-01-14). "Jennifer Ahern, Ziad Obermeyer named Chan Zuckerberg Biohub Investigators" (in en-US). https://publichealth.berkeley.edu/articles/spotlight/faculty/jennifer-ahern-ziad-obermeyer-named-chan-zuckerberg-biohub-investigators. 
  3. ↑ "NBER Appoints 54 Research Associates, 3 Faculty Research Fellows" (in en). 2023-10-02. https://www.nber.org/news/nber-appoints-54-research-associates-3-faculty-research-fellows. 
  4. ↑ "TIME100 AI 2023: Ziad Obermeyer" (in en). Time. 2023-09-07. https://time.com/collection/time100-ai/6308242/dr-ziad-obermeyer/. 
  5. ↑ "Racial bias found in widely used health care algorithm" (in en). 2019-11-07. https://www.nbcnews.com/news/nbcblk/racial-bias-found-widely-used-health-care-algorithm-n1076436. 
  6. ↑ "Featured Researcher: Ziad Obermeyer" (in en). https://www.nber.org/bh/20241/featured-researcher-ziad-obermeyer. 
  7. ↑ "Ziad Obermeyer, M.D., M.Phil. | AcademyHealth". https://academyhealth.org/about/people/ziad-obermeyer-md-mphil. 
  8. ↑ 8.0 8.1 "Ziad Obermeyer" (in en-US). 2019-07-16. https://publichealth.berkeley.edu/people/ziad-obermeyer. 
  9. ↑ "The Question of Care | Harvard Medicine Magazine" (in en). https://magazine.hms.harvard.edu/articles/question-care. 
  10. ↑ Span, Paula (2014-11-20). "An Easier Death, and Less Costly, Too" (in en). https://archive.nytimes.com/newoldage.blogs.nytimes.com/2014/11/20/an-easier-death-and-less-costly-too/. 
  11. ↑ "Clinical and Machine Learning Expert, Dr. Ziad Obermeyer Joins GNS Healthcare's Strategic Advisory Board" (in en-US). 2018-09-18. https://www.biospace.com/clinical-and-machine-learning-expert-dr-ziad-obermeyer-joins-gns-healthcare-s-strategic-advisory-board. 
  12. ↑ "2021- Ziad Obermeyer - College of Arts and Sciences - Santa Clara University". https://www.scu.edu/denardo/lecture/2021--ziad-obermeyer/. 
  13. ↑ "Ziad Obermeyer | The Institute for Quantitative Social Science" (in en). https://www.iq.harvard.edu/people/ziad-obermeyer. 
  14. ↑ Obermeyer, Ziad (2025-04-01). "CV". https://ziadobermeyer.com/wp-content/uploads/2025/04/ZO_CV_2pp-3.pdf. 
  15. ↑ "Ziad Obermeyer named one of TIME's top 100 leaders in artificial intelligence" (in en-US). 2023-09-08. https://publichealth.berkeley.edu/articles/spotlight/faculty/ziad-obermeyer-named-one-of-times-top-100-leaders-in-ai. 
  16. ↑ "The legal doctrine that will be key to preventing AI discrimination" (in en-US). https://www.brookings.edu/articles/the-legal-doctrine-that-will-be-key-to-preventing-ai-discrimination/. 
  17. ↑ "Science" (in en). doi:10.1126/science.aaz3873. https://www.science.org/action/cookieAbsent. 
  18. ↑ Moor, Michael; Banerjee, Oishi; Abad, Zahra Shakeri Hossein; Krumholz, Harlan M.; Leskovec, Jure; Topol, Eric J.; Rajpurkar, Pranav (2023-04-01). "Foundation models for generalist medical artificial intelligence" (in en). Nature 616 (7956): 259–265. doi:10.1038/s41586-023-05881-4. ISSN 1476-4687. https://www.nature.com/articles/s41586-023-05881-4. 
  19. ↑ Jain, Sneha S.; Elias, Pierre; Poterucha, Timothy; Randazzo, Michael; Lopez Jimenez, Francisco; Khera, Rohan; Perez, Marco; Ouyang, David et al. (2024-06-18). "Artificial Intelligence in Cardiovascular Care—Part 2: Applications". JACC 83 (24): 2487–2496. doi:10.1016/j.jacc.2024.03.401. PMID 38593945. PMC 12215878. https://www.jacc.org/doi/full/10.1016/j.jacc.2024.03.401. 
  20. ↑ "Racial Bias Found in Algorithms That Determine Health Care for Millions of Patients - IEEE Spectrum" (in en). https://spectrum.ieee.org/racial-bias-found-in-algorithms-that-determine-health-care-for-millions-of-patients. 
  21. ↑ Mullainathan, Sendhil (2019-12-06). "Biased Algorithms Are Easier to Fix Than Biased People" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2019/12/06/business/algorithm-bias-fix.html. 
  22. ↑ "Artificial Intelligence in Health Care" (in en-US). The Washington Post. 2021-01-05. https://www.washingtonpost.com/washington-post-live/2021/01/13/artificial-intelligence-health-care/. 
  23. ↑ Fitzpatrick, Lisa (2019-11-01). "Underserved Populations Have Access To Technology- Why Is No One Investing in Them?". Forbes. https://www.forbes.com/sites/lisafitzpatrick/2019/11/01/underserved-populations-have-access-to-technologywhy-is-no-one-investing-in-them/. 
  24. ↑ Mullainathan, Sendhil; Obermeyer, Ziad (2022-04-08). "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care" (in en). The Quarterly Journal of Economics 137 (2): 679–727. doi:10.1093/qje/qjab046. ISSN 0033-5533. PMID 41054600. PMC 12497445. https://academic.oup.com/qje/article/137/2/679/6449024. 
  25. ↑ Agarwal, Nikhil; Moehring, Alex; Rajpurkar, Pranav; Salz, Tobias (2023-07-01), Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology, Working Paper Series, National Bureau of Economic Research, doi:10.3386/w31422, 31422, https://www.nber.org/papers/w31422, retrieved 2026-03-04 
  26. ↑ Jain, Sneha; Elias, Pierre; Poterucha, Timothy. "Artificial Intelligence in Cardiovascular Care—Part 2: Applications". Journal of the American College of Cardiology (JACC). https://www.jacc.org/doi/full/10.1016/j.jacc.2024.03.401. 
  27. ↑ Das, Jishnu; Do, Quy-Toan (2023-05-01). "The Prices in the Crises: What We Are Learning from 20 Years of Health Insurance in Low- and Middle-Income Countries" (in en). Journal of Economic Perspectives 37 (2): 123–152. doi:10.1257/jep.37.2.123. ISSN 0895-3309. https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.37.2.123. 
  28. ↑ Kakani, Pragya; Chandra, Amitabh; Mullainathan, Sendhil; Obermeyer, Ziad (2020-09-08). "Allocation of COVID-19 Relief Funding to Disproportionately Black Counties". JAMA 324 (10): 1000–1003. doi:10.1001/jama.2020.14978. ISSN 1538-3598. PMID 32897336. 
  29. ↑ Ross, Casey (2020-08-07). "Study finds racial bias in the government's formula for distributing Covid-19 aid to hospitals" (in en-US). https://www.statnews.com/2020/08/07/racial-bias-in-government-covid19-hospital-aid-formula/. 
  30. ↑ Thomasian, Nicole M.; Eickhoff, Carsten; Adashi, Eli Y. (2021-12-01). "Advancing health equity with artificial intelligence". Journal of Public Health Policy 42 (4): 602–611. doi:10.1057/s41271-021-00319-5. ISSN 1745-655X. PMID 34811466. PMC 8607970. https://pmc.ncbi.nlm.nih.gov/articles/PMC8607970/. 
  31. ↑ Mody, Aaloke; Pfeifauf, Kristin; Bradley, Cory; Fox, Branson; Hlatshwayo, Matifadza G; Ross, Will; Sanders-Thompson, Vetta; Joynt Maddox, Karen et al. (2021-11-02). "Understanding Drivers of Coronavirus Disease 2019 (COVID-19) Racial Disparities: A Population-Level Analysis of COVID-19 Testing Among Black and White Populations" (in en). Clinical Infectious Diseases 73 (9): e2921–e2931. doi:10.1093/cid/ciaa1848. ISSN 1058-4838. PMID 33315066. PMC 7799327. https://academic.oup.com/cid/article/73/9/e2921/6033727. 
  32. ↑ Alsan, Marcella; Chandra, Amitabh; Simon, Kosali I. (2021-06-01), The Great Unequalizer: Initial Health Effects of COVID-19 in the United States, Working Paper Series, National Bureau of Economic Research, doi:10.3386/w28958, 28958, https://www.nber.org/papers/w28958, retrieved 2026-03-05 
  33. ↑ Wrigley-Field, Elizabeth (2020-08-24). "US racial inequality may be as deadly as COVID-19". Proceedings of the National Academy of Sciences (PNAS) 117 (36). https://doi.org/10.1073/pnas.2014750117. 
  34. ↑ Pierson, Emma; Cutler, David M.; Leskovec, Jure; Mullainathan, Sendhil; Obermeyer, Ziad (2021-01-13). "An algorithmic approach to reducing unexplained pain disparities in underserved populations" (in en). Nature Medicine 27 (1): 136–140. doi:10.1038/s41591-020-01192-7. ISSN 1546-170X. https://www.nature.com/articles/s41591-020-01192-7. 
  35. ↑ Gichoya, Judy Wawira; Celi, Leo Anthony (2021-12-01). "Beyond the AJR: "An Algorithmic Approach to Reducing Unexplained Pain Disparities in Underserved Populations"" (in en). American Journal of Roentgenology 217 (6): 1480–1480. doi:10.2214/AJR.21.26020. ISSN 0361-803X. PMC 9159490. https://www.ajronline.org/doi/10.2214/AJR.21.26020. 
  36. ↑ "Racial bias in a medical algorithm favors white patients over sicker black patients" (in en-US). The Washington Post. 2019-10-24. ISSN 0190-8286. https://www.washingtonpost.com/health/2019/10/24/racial-bias-medical-algorithm-favors-white-patients-over-sicker-black-patients/. 
  37. ↑ King, Robert (2019-10-28). "New York insurance regulator to probe Optum algorithm for racial bias | Fierce Healthcare" (in en). https://www.fiercehealthcare.com/payer/new-york-to-probe-algorithm-used-by-optum-for-racial-bias. 
  38. ↑ Simonite, Tom. "Senators Protest a Health Algorithm Biased Against Black People" (in en-US). Wired. ISSN 1059-1028. https://www.wired.com/story/senators-protest-health-algorithm-biased-against-black-people/. 
  39. ↑ "Booker, Wyden Demand Answers on Biased Health Care Algorithms | U.S. Senator Cory Booker of New Jersey" (in en). https://www.booker.senate.gov/news/press/booker-wyden-demand-answers-on-biased-health-care-algorithms. 
  40. ↑ Wetsman, Nicole (2019-12-04). "There's no quick fix to find racial bias in health care algorithms" (in en-US). https://www.theverge.com/2019/12/4/20995178/racial-bias-health-care-algorithms-cory-booker-senator-wyden. 
  41. ↑ Center for Applied AI at Chicago Booth. "Algorithmic Bias Playbook". https://www.ftc.gov/system/files/documents/public_events/1582978/algorithmic-bias-playbook.pdf. 
  42. ↑ Wang, H. Echo; Weiner, Jonathan P.; Saria, Suchi; Kharrazi, Hadi (2024-04-18). "Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis" (in EN). Journal of Medical Internet Research 26 (1). doi:10.2196/47125. PMC 11066744. https://www.jmir.org/2024/1/e47125. 
  43. ↑ "Artificial Intelligence and Health Care: Promise and Pitfalls | The United States Senate Committee on Finance" (in en). https://www.finance.senate.gov/hearings/artificial-intelligence-and-health-care-promise-and-pitfalls. 
  44. ↑ "Hearing Wrap Up: Innovative Technology Can Lower Healthcare Costs for Americans" (in en). 2026-03-04. https://oversight.house.gov/release/hearing-wrap-up-innovative-technology-can-lower-healthcare-costs-for-americans/. 
  45. ↑ Simonite, Tom. "When It Comes to Health Care, AI Has a Long Way to Go" (in en-US). Wired. ISSN 1059-1028. https://www.wired.com/story/health-care-ai-long-way-to-go/. 
  46. ↑ Ross, Casey (2023-06-21). "Amid the AI gold rush, a new company forms to vet models and root out weaknesses" (in en-US). https://www.statnews.com/2023/06/21/ai-dandelion-health-algorithms-hospitals/. 
  47. ↑ "Dandelion Health launches pilot to evaluate AI performance and potential bias | Healthcare IT News" (in en). 2023-06-21. https://www.healthcareitnews.com/news/dandelion-health-launches-pilot-evaluate-ai-performance-and-potential-bias. 
  48. ↑ Obermeyer, Ziad (2024-02-05). "Senate Testimony for Artificial Intelligence and Health Care: Promise and Pitfalls". https://www.finance.senate.gov/imo/media/doc/02042024_obermeyer_testimony.pdf. 
  49. ↑ "The American Heart Association AI Assessment Lab, Powered by Dandelion Health" (in en). https://www.heart.org/en/professional/quality-improvement/aha-ai-assessment-lab. 
  50. ↑ "Bedside to Bench: Reinventing Medicine with AI - September 18, 2025" (in en-US). https://schaeffer.usc.edu/event/bedside-to-bench-reinventing-medicine-with-ai/. 
  51. ↑ "TIME100 AI 2023: Ziad Obermeyer" (in en). Time. 2023-09-07. https://time.com/collection/time100-ai/6308242/dr-ziad-obermeyer/. 
  52. ↑ Svoboda, Dylan (2022-01-14). "Jennifer Ahern, Ziad Obermeyer named Chan Zuckerberg Biohub Investigators" (in en-US). https://publichealth.berkeley.edu/articles/spotlight/faculty/jennifer-ahern-ziad-obermeyer-named-chan-zuckerberg-biohub-investigators. 
  53. ↑ "NBER Appoints 54 Research Associates, 3 Faculty Research Fellows" (in en). 2023-10-02. https://www.nber.org/news/nber-appoints-54-research-associates-3-faculty-research-fellows. 
  54. ↑ "National Academy of Medicine Announces 10 Emerging Leaders in Health and Medicine Scholars - NAM" (in en-US). 2020-05-05. https://nam.edu/news-and-insights/national-academy-of-medicine-announces-10-emerging-leaders-in-health-and-medicine-scholars/. 
  55. ↑ "2021 ASHEcon Award Winners – ASHEcon" (in en-US). https://www.ashecon.org/ashecon-2021/2021-ashecon-award-winners/. 
  56. ↑ Jack, Andrew (2022-01-22). "Academic research award: smart ideas with real-world impact". Financial Times. https://www.ft.com/content/d5ca2a09-333b-4f86-9e99-3a3bf1b86c75. 



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