AI for Health

AI for Health — Azure Credit Award

Supports global health nonprofits and researchers with AI compute resources and scientific collaboration opportunities.

AI for Health is a flagship philanthropic program of the Microsoft AI for Good Lab, launched in January 2020, that provides Azure cloud computing credits and dedicated Microsoft AI researcher collaboration time to nonprofits, academic institutions, and individual researchers working on global health challenges. The program accepts applications on a rolling, year-round basis with no published deadline. Since its launch, the program has partnered with more than 200 grantees worldwide across a breadth of health domains. No cash grants are disbursed; the entire award consists of in-kind Azure compute credits and Microsoft AI scientist collaboration hours.

The program targets three primary research domains: population health analytics (integrating multi-sector health data to illuminate disease drivers), imaging analytics (applying AI to image-based clinical data for diagnostics), and genomics and proteomics (predicting disease risks and identifying protein intervention targets). Documented focus areas include health equity, cancer detection for pancreatic, breast, and prostate cancers, smoking cessation, and cardiovascular risk factor analysis. Eligibility is open globally to nonprofits, academic institutions, and researchers; for-profit organizations are not identified as an eligible pathway. Past partners include institutions collaborating on AI4HealthyCities with the Novartis Foundation and multiple university research groups.

Applications are submitted directly to the AI for Good Lab and evaluated continuously by Microsoft researchers. Because no application portal URL was publicly listed on the program page at the time of research, prospective applicants should contact the lab through the AI for Good website to confirm the current submission process. The program is well-suited to research teams with defined AI or data science use cases in health who need computational resources rather than cash funding.