Stanford Engineering
AI Health

AI Health

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AI for Health

WELCOME TO
James Zou at the lectern at the 2024 AI for Health Annual Meeting

The 2024 AI for Health Annual Meeting

Hear Prof. James Zou

Explore the AI for Health team's discoveries in expanding and leveraging LLMs to make a real impact across different healthcare challenges, keeping in mind the main stakeholders: clinicians, patients, and researchers. Each of these groups have unique problems and AI for Health is developing different types of algorithms adapted to their different needs.

See James Zou's lecture

The Mission of AI for Health

The mission of AI for Health is to develop unbiased, explainable AI algorithms to better understand health and wellness, to improve the efficiency, value and delivery of healthcare and to improve patient experience and outcomes.

Artificial Intelligence has had a large and demonstrated impact on efficiency and profitability for many industries, but its power is only just now being utilized in health applications. AI systems in health must be unbiased, explainable, efficient, domain-specific and accessible to patients, practitioners, researchers and business users.

The research performed through the AI for Health Affiliates Program aims to address these challenges in human health by advancing domain-specific AI technologies in healthcare administration, healthcare delivery, wellness, and more.

Healthcare Administration

  • Natural language translations and understanding of medical terminologies
  • Recommendation systems for healthcare products and applications
  • Improved healthcare operations
  • Customer and patient satisfaction

Healthcare Delivery

  • Healthcare predictions
  • Clinician decision support systems
  • Drug interactions, repurposing and discovery

Wellness

  • Social and medical networks
  • Understanding wearables
  • Mining search data 
  • Patient sentiment analysis

The research performed by AI for Health centers around flagship project themes with applications across health domains and industries. These flagship projects aim to develop methodologies with strong applicability to real-world interests through collaborations between Stanford faculty across the Schools of Medicine and Engineering with insights provided by our Corporate Affiliates.

ALTE: AI for Literacy, Transparency and Engagement

The goal of this flagship project is to advance patient literacy, engagement and healthcare transparency through natural language processing of medical text and general jargon or layperson descriptions of medical conditions. Success of this flagship will enable patients to be better informed in making healthcare decisions, decrease call center and provider time in translating medical terminologies and ultimately provide better care outcomes and value.

James Zou at the lectern at the 2022 SAIL Annual Meeting

James Zou

James Zou is an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and Electrical Engineering at Stanford University. He works on making machine learning more reliable, human-compatible and statistically rigorous, and is especially interested in applications in human disease and health. Several of his team’s algorithms are widely used in tech and biotech industries. Mr. Zou received a Ph.D from Harvard in 2014, and was a member of Microsoft Research, a Gates Scholar at Cambridge and a Simons fellow at UC Berkeley. He joined Stanford in 2016 and is excited to be a two-time Chan-Zuckerberg Investigator and the faculty director of the university-wide Stanford Data4Health hub. He also a member of the Stanford AI Lab. His research is supported by the Sloan Fellowship, the NSF CAREER Award, and Google, Amazon and Adobe AI awards. 

Meet the AI for Health Team

The AI for Health lab is currently working on making generative AI agents more capable and reliable, and applying them to tackle important problems in healthcare, biology, chemistry and medicine. The founding team is deeply committed to building collaboration with industry partners to pioneer solutions that address real-world challenges.

James Zou photo

James Zou, Director for AI for Health

The Team

  • Russ Altman

    Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine (General Medical Discipline), of Biomedical Data Science

  • Chris Re

    Associate Professor of Computer Science

  • Daniel Rubin

    Professor of Biomedical Data Science, of Radiology and of Medicine

  • Nigam Shah

    Associate Professor of Medicine and of Biomedical Data Science

AI for Health invites corporate engagement

The AI for Health program welcomes engagement from industry to provide insight into real-world use cases and funding to drive solutions with a global impact. Partner with AI for Health through our Affiliates Program and accelerate innovation. Contribute to the definition of research projects, recruit Stanford students, send a visiting scholar: these are just some of the opportunities membership to our Affiliates Program will offer. Contact Joseph.Huang [a] Stanford.edu to learn more.

Scenes from an Affiliates Program event

AI for Health: be at the frontier of innovation

Take a look inside an Affiliates Annual Event

A key benefit of membership to the AI for Health Affiliate Program is access to exclusive events that inspire and connect. Hosted on the beautiful Stanford campus, they are designed to take members on a deep dive into innovation while encouraging casual networking.

Check out the 2024 Annual Meeting for Affiliates