On Friday 28 November, the Center for Resilient Health (CRH) at the Stockholm School of Economics welcomed Ziad Obermeyer, Associate Professor and Blue Cross of California Distinguished Professor at the Berkeley School of Public Health, for a paper seminar titled “Bedside to Bench: Reinventing Medicine with AI.”

The seminar was part of CRH's ongoing "Paper Seminar" series connecting research in health, economics, and technology.

From bedside to bench: a new flow of discovery

In his talk, Professor Obermeyer explored how AI is transforming medicine into a data-driven science, capable of extracting insights from high-dimensional signals like medical images and ECG waveforms - data that humans alone struggle to interpret.

Using data from Region Halland in Sweden, collected in collaboration with Torkel Strömsten, Associate Professor at SSE, Obermeyer demonstrated how machine learning models trained on more than 400,000 ECGs can predict sudden cardiac death before symptoms appear.

These algorithms, he explained, don't just help doctors make better predictions, they also open up new lines of scientific inquiry - linking patterns in the data to underlying biological mechanisms.

“Rebooting the flow of ideas from bedside to bench, from clinical observation to theoretical discovery, is one of AI's most exciting promises,” Obermeyer said. “It will transform patient care, accelerate drug discovery, and generate enormous economic and social value.”

Medicine meets economics

While his research is deeply rooted in medicine, Obermeyer emphasized that AI's implications reach far beyond healthcare.

Machine learning will change how economists study health systems, how incentives are designed, and how innovation is evaluated. As diagnostic tests become cheaper and more decentralized - even smartphone-based - health systems will face structural shifts in cost, access, and accountability.

The talk sparked strong engagement from fellow SSE researchers and faculty. Many highlighted how Obermeyer's work bridges data science, medicine, and economics, reflecting CRH's mission to promote interdisciplinary approaches to health and sustainability.

“Ziad's research challenges both medical and economic thinking,” said Karl Wennberg, Professor at SSE and co-director of CRH. “It shows how algorithms can uncover patterns invisible to humans - and how economists can help ensure these technologies are used responsibly.”

About Ziad Obermeyer

Ziad Obermeyer is a physician, researcher, and Associate Professor at the University of California, Berkeley, where he co-leads the Computational Precision Health program with the University of California, San Francisco (UCSF).

His research investigates how machine learning can improve medical decision-making and reveal new mechanisms of disease, but also how algorithms can reinforce bias if used without scrutiny.

His work has influenced health policy, regulation, and AI governance worldwide and has been presented to the U.S. Senate Finance Committee.