Ziad Obermeyer occupies a unique position where the realms of machine learning meet healthcare. His research is concentrated on leveraging machine learning to aid medical professionals in making improved decisions—such as determining the right candidates for heart attack assessments. Furthermore, he aims to empower researchers to uncover new insights by interpreting data through an algorithmic lens—including identifying overlooked pain causes or correlating individual body temperature norms with health outcomes. His investigations have also highlighted the widespread automation of racial biases by algorithms that impact millions. This vital work has significantly influenced how organizations approach algorithm development and the standards by which lawmakers and regulators ensure AI operates ethically.
At the UC Berkeley School of Public Health, Ziad Obermeyer serves as the distinguished Blue Cross of California Associate Professor specializing in Health Policy and Management. His scholarly pursuits bridge health policy, medicine, and technology.machine learning While he was a junior faculty member at Harvard Medical School, Ziad was honored with the Early Independence Award, a prestigious recognition from the National Institutes of Health for exceptional emerging scientists. In addition to his academic work, he continues to serve as an emergency physician in underserved regions of the U.S. Ziad's prior experience includes consulting roles at McKinsey & Co. in New Jersey, Geneva, and Tokyo, specifically assisting pharmaceutical and global health clients before transitioning into medicine.
MD – Harvard Medical School
MPhil – Cambridge
BA – Harvard College
2023
Ziad Obermeyer's extensive research primarily investigates the intersection of machine learning and healthcare. His work includes large-scale data evaluation and supporting doctors in their decision-making processes. In his recent findings, he uncovered a disparity in healthcare costs between Black and White patients facing similar medical challenges, stemming from unequal access to healthcare. This discrepancy in prioritization has led algorithms to misclassify patients based on racial lines.
Ziad has undertaken efforts to rectify this systemic issue, which is referred to as 'label choice bias,' aiming to reduce racial healthcare inequalities. He believes that by retraining these algorithms to make more equitable predictions, we can not only diminish disparities but also reallocate resources to provide better care for those who need it the most. In his words, 'We can enhance algorithmic functions to work for the greater good by reprogramming them to predict more equitable outcomes.'
London-based Phare Health, an AI-centric company, secured €2.8 million in seed funding led by General Catalyst. This capital will propel AI solutions and establish collaborations within the UK’s National Health Service as well as internationally. The firm seeks to streamline operational efficiency by integrating AI into non-clinical functions, effectively linking healthcare professionals with hospital operations. Furthermore, the funding will aid in growing Phare Health's workforce.
AstraZeneca has introduced Evinova, a health-tech division aimed at merging digital tools with clinical trials.artificial intelligence This new division is designed to cut costs and reduce the duration of trials, while also minimizing participant recruitment challenges. Evinova plans to combine AstraZeneca's expertise in clinical and regulatory matters with cutting-edge digital technology. Among other offerings, the division will provide tailored scientific services, continual patient monitoring, trial framework design, and advisory services. The digital health market is predicted to reach a monumental $900 billion by the year 2032.
Recent social media updates from Ziad Obermeyer
What an inspiring narrative—highlighting the strength of open data combined with a unified objective! https://t.co/fkcY4u1Dnc
There's an immense amount of unacknowledged effort that goes into pulling this together – credit goes to the entire team. @GoogleDeepMindhttps://t.co/wOkiVimR5e
I came across a fantastic thread discussing how math education can intertwine with life sciences—I only wish I had the chance to do this in biology class instead of just memorizing information about Golgi apparatuses. https://t.co/xCsLkA9KJB
AI is revealing itself in diverse forms within the healthcare landscape, from uncovering novel genetic connections to enhancing robotic surgical systems.
