Filter by Topic: All Insights | CAIA’s Mission |Federated Learning| Data Standardization| CAIA’s Projects
How CAIA enables cross-organizational research through collaborative data standardization
In this blog post, we’ll take you behind the scenes of this standardization process, exploring the data mapping and cross-institutional coordination required to make multi-site federated learning work.
Inside the Cancer AI Alliance: Dr. Srinivasan Yegnasubramanian on a proactive vision of cancer care
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.
Why CAIA prioritizes a platform approach to scale cancer research
Learn how how the Cancer AI Alliance (CAIA) is leading a transformation in cancer research by enabling a platform approach across multiple institutions.
How MSK and CAIA use federated learning to uncover EHR data patterns
Discover how Memorial Sloan Kettering (MSK) and the Cancer AI Alliance (CAIA) use federated learning to transform noisy EHR data into predictive patient timelines.
CAIA’s first wave of cancer discovery: 8 pilot projects powered by our federated learning platform
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.
A blueprint for scaling research: 5 operational principles driving the Cancer AI Alliance (CAIA)
We offer a look at the challenges and barriers that the CAIA team dismantled in our mission to accelerate cancer research and discovery.
How the Cancer AI Alliance operationalizes secure, multi-site research with federated learning
We explore the federated learning components that help CAIA securely operationalize our multi-site AI training approach.
The role of data federation in cancer research: 4 Insights from the 2025 Deloitte AI Forum
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.
A common format for federated learning: CAIA’s approach to data standardization
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.
How the orchestration layer securely enables AI insights in a federated learning framework
In this post, we take a closer look at the technology (enabled by NVIDIA FLARE) that underlies CAIA’s secure orchestration layer.
Cancer AI Alliance unveils first collaborative AI platform for cancer research
Secure, scalable, multi-cloud platform using federated learning aims to accelerate cancer discoveries and treatments
How CAIA harnesses federated learning for secure collaboration
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.
Research beyond silos: How CAIA is accelerating the fight against cancer
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.