In this interview, we feature a conversation between Brian M. Bot (Director of the Strategic Coordinating Center for CAIA) and Dr. Srinivasan “Vasan” Yegnasubramanian (Professor at Johns Hopkins and Director of inHealth Precision Medicine). They discuss CAIA’s beginnings, our federated learning model to enable cross-institutional cancer research, and how we can move to a more proactive form of care. 

Learn how how the Cancer AI Alliance (CAIA) is leading a transformation in cancer research by enabling a platform approach across multiple institutions. 

Discover how Memorial Sloan Kettering (MSK) and the Cancer AI Alliance (CAIA) use federated learning to transform noisy EHR data into predictive patient timelines.

Learn more about the unique pilot projects that CAIA is currently piloting across its federated network with researchers from Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, and Fred Hutch Cancer Center.

Read more from CAIA’s panel at the 2026 J.P. Morgan Healthcare conference about how CAIA is accelerating the future of cancer care by federating data across cancer centers, helping researchers solve problems together instead of in isolation. 

We offer a look at the challenges and barriers that the CAIA team dismantled in our mission to accelerate cancer research and discovery.

We explore the federated learning components that help CAIA securely operationalize our multi-site AI training approach.

In this blog post, we offer 4 takeaways from Pete Shimer and Brian M. Bot’s conversation at the 2025 Deloitte AI Forum on the Cancer AI Alliance’s data federation framework.

In this blog post, we take a look at CAIA’s data standardization process, and how we adhere to privacy, regulatory, ethical and legal guidelines.

Learn about CAIA’s announcements at Madrona’s IA40 summit, including the progress we have made over the last year.

In this post, we take a closer look at the technology (enabled by NVIDIA FLARE) that underlies CAIA’s secure orchestration layer. 

Secure, scalable, multi-cloud platform using federated learning aims to accelerate cancer discoveries and treatments

How can we train  AI models on the collective knowledge of the nation's top cancer centers without exposing sensitive patient information? 

CAIA’s solution is an AI training approach called federated learning.

Unlocking clues in patient data is a key opportunity in modern healthcare, but institutional silos and privacy concerns often slow progress. The Cancer AI Alliance (CAIA) unites leading cancer centers and AI technology pioneers to solve this.