The AI for Health Institute (AIHealth) brings together AI researchers and health investigators to forge new paths to solve significant health problems with advanced AI technologies.

Call for Proposals

We are launching the AI for Health Seed Funding program in partnership with the Here and Next Seed Grant initiative. The application portal is now open, with a submission deadline of March 1, 2025.

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Talk: Advancing Children and Youth Health with Artificial Intelligence

Talk: Advancing Children and Youth Health with Artificial Intelligence

Chenyang Lu to Discuss Leveraging AI to Tackle Critical Challenges in Health Care, Global Health, and Public Health at WashU’s Children and Youth Collaborative Network

$5 million NIH grant to find causes of chronic pain after surgery

$5 million NIH grant to find causes of chronic pain after surgery

Study may lead to personalized prevention, management tools for post-operative pain.

Analyzing multiple mammograms improves breast cancer risk prediction

Analyzing multiple mammograms improves breast cancer risk prediction

AI method spots subtle changes over time, enhances accuracy of determining five-year risk

Researchers define new subtypes of common brain disorder

Researchers define new subtypes of common brain disorder

Artificial intelligence identified 3 subtypes of Chiari type-1 malformations, could improve medical decision making

AI for Health Institute hosts inaugural symposium on AI for health

AI for Health Institute hosts inaugural symposium on AI for health

Interdisciplinary event brings together AI and health experts to examine AI’s transformative role in health care and public health.

AI for Health Seed Grant

AI for Health Seed Grant

We are launching the AI for Health Seed Funding program in partnership with the Here and Next Seed Grant initiative. The application portal is now open, with a submission deadline of March 1, 2025.

School of Medicine joins AI collaborative 

School of Medicine joins AI collaborative 

Drs. Payne and Maddox named corps site leads

Deep learning models can be trained with limited data

Deep learning models can be trained with limited data

Ulugbek Kamilov, graduate students, develop method that could reduce errors in computational imaging

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