AI in Health
Overview
Research labs and clinical settings are continually generating data, data that have until now been inaccessible and for many complex reasons, difficult to use. If gathered intentionally and deciphered using AI and machine learning tools, this data could further science’s understanding of a range of diseases, advance translational research, and enhance clinical care. The field is moving quickly, making big strides possible. There are, however, specific challenges that need to be overcome urgently before the promise of AI and machine learning tools can be realized to fully and equitably benefit human health.
Science and health funders are at a crossroads where thoughtful investments at the intersection of AI and health can be a catalyst to improve the health and well-being of the global community. Milken Institute SPARC has been bringing together funders and stakeholders from across disciplines to engage and build strong, thoughtful partnerships to grow the field in ways that lead to transformative science and health discoveries.
Computational Biology Program
Computational Biology at the Milken Institute
The Biswas Family Foundation partnered with the Milken Institute Science Philanthropy Accelerator for Research and Collaboration (SPARC) in 2023 to conduct a comprehensive review of the computational biology field and identify areas of opportunity for philanthropy to advance the integration of computational tools in biomedical research and clinical care.
Using the findings and insights from this analysis, which are published in Transformative Computational Biology, a Giving Smarter Guide, the Biswas Family Foundation developed the Transformative Computational Biology Grant Program, which is focused on accelerating the use of computational approaches in translational research and clinical settings to improve the well-being and health of the global community.
Funded Research
- $14 million in funding awarded as of 2024
- 5 scientific research grants supported
- 15 multidisciplinary investigators supported