Ziad Obermeyer works at the intersection of machine learning and health. His research focuses on how machine learning can help doctors make better decisions (like whom to test for heart attack), and help researchers make new discoveries—by ‘seeing’ the world the way algorithms do (like finding new causes of pain that doctors miss, or linking individual body temperature set points to health outcomes). He has also shown how widely-used algorithms affecting millions of patients automate and scale up racial bias. That work has impacted how many organizations build and use algorithms, and how lawmakers and regulators hold AI accountable.
At the UC Berkeley School of Public Health, Ziad Obermeyer holds the Blue Cross of California Distinguished Associate Professorship in Health Policy and Management. He does research at the nexus of health policy, medicine, and machine learning. The National Institutes of Health’s most prestigious honor for outstanding junior scientists, the Early Independence honor, was given to him while he was an assistant professor at Harvard Medical School. He is still an emergency physician in underprivileged areas in the United States. He was employed by McKinsey & Co. in New Jersey, Geneva, and Tokyo as a consultant for pharmaceutical and global health companies before beginning his medical profession.
MD – Harvard Medical School
MPhil – Cambridge
BA – Harvard College
2023
Obermeyer’s broad research mainly focuses on the nexus between machine learning and health, including large-scale data analysis and supporting physicians in their decision-making. According to Obermeyer’s latest study, there is a cost difference between Black and White patients with similar demands as a result of Black patients’ uneven access to healthcare. These two groups of patients are categorized as having different priorities, and algorithms are able to identify this difference and assign a lower priority to Black patients.
Obermeyer set out to correct this biased proxy mechanism known as “label choice bias” in order to address these amplified racial inequalities and clear the path for access to more egalitarian care. “We can make algorithms work for the better by retraining them to predict less biased proxies, which will reduce disparities rather than widen them and reallocate resources to those who need them,” Obermeyer wrote in an email.
Phare Health, a London-based AI company, has closed a €2.8 million seed funding round led by General Catalyst. The funding will be used to drive AI innovation and foster partnerships within the UK’s National Health Service and internationally. The company aims to enhance operational efficiency by leveraging AI for non-clinical tasks, bridging the gap between clinicians and hospital operations. The funding will also support the expansion of Phare Health’s team.
AstraZeneca has launched Evinova, a health-technology division integrating digital solutions and artificial intelligence into clinical trials. The division aims to reduce costs and duration for trials, reducing recruitment requirements. Evinova will integrate AstraZeneca’s clinical and regulatory experience with digital technology expertise. The division will offer personalized scientific services, remote patient monitoring, trial design, and consulting services. The digital health market is expected to reach $900 billion by 2032.
Latest Social posts of Ziad Obermeyer
What an amazing story—the power of open data + a common goal to work towards! https://t.co/fkcY4u1Dnc
wonderful thread on teaching math integrated with life sciences–I wish I'd done this in biology, in addition to memorizing facts about Golgi bodies https://t.co/xCsLkA9KJB
