The scale of CAIA's federated learning approach is important when it comes to studying conditions where data is scarce.
Learn more about how we are enabling research into a rare form of brain cancer at @HopkinsMedicine!
https://t.co/pC80I5PPYb
Check out our new blog post featuring a conversation between two of CAIA's leaders on how AI could be used to ensure that every individual receives consistent, expert, and precise care and how we can move to a more proactive form of cancer care.
https://t.co/pC80I5PPYb
At @googlecloud Next, CAIA's Brian M. Bot discussed how we are leading a transformation in cancer research by supporting a multitude of projects simultaneously with our federated learning platform!
https://t.co/r6w0nSCW1J
In our latest blog , we are taking a closer look at one of CAIA's AI innovation projects, featuring a team from @MSKCancerCenter!
The goal is to bring together the entire history of a patient to predict the next phase of their medical experience.
https://t.co/wQNnvQogdk
In our latest blog, we highlight the projects that CAIA is piloting across its federated network with researchers from @MSKCancerCenter, @DanaFarber, and @fredhutch!
Learn how our federated learning platform is a working engine for AI discovery!
https://t.co/nYlUAKahuQ
Read about how @fredhutch researchers @DrSimoneDekker and Dr. Stephen Salerno are navigating real-world cancer data analysis using AI in collaboration with @allen_ai !
https://t.co/6aMrk3BYCU
Delighted to see CAIA's projects featured in a recent news story by @fredhutch!
Read about the work @CGrassberger is doing to predict high-risk bone metases using federated learning! https://t.co/6aMrk3BYCU
💻 #ASCOAI: Can federated learning accelerate #precisiononcology?
The Cancer AI Alliance (@CAIAorg) is training #AI models across 4 NCI cancer centers—without sacrificing patient privacy.
📝️ Interview with Eliezer Van Allen, MD (@VanAllenLab)
🔗 https://t.co/ufy1FpPzvl
Scaling Data driven cancer research is fundamentally a systems problem not just a modeling one.
This article provides a clear, operational blueprint for doing federated, multi-institutional research right. Highly recommended read.
In our latest blog post, we offer a look at the challenges and barriers that the CAIA team dismantled in our mission to accelerate cancer research and discovery.
Read our 5 operational principles for scaling cancer research:
https://t.co/Bjl6MsJDAo
Wed, Jan 14: @JHUBME Prof @alexisjbattle joins her CAIA colleagues at J.P. Morgan's Healthcare Conference in San Fran for the "Federated AI in Oncology" panel. You can watch the livestream. Noon ET. Register: https://t.co/YK16W5JjvW
We are kicking off 2026 with a panel at the @jpmorgan Healthcare Conference in San Francisco!
The panel will highlight CAIA’s progress by demonstrating how its federated learning model securely accelerates research through real-world oncology projects.
Sometimes big ideas are works of art.
Welcome to the third season of (ART)ificial Intelligence, where we sat down with industry leaders at @MadronaVentures IA40 Summit to redesign, rethink and reimagine the world around us.
Check it out here: https://t.co/t4w4UvEit1
CAIA's participating cancer centers maintain full control, executing code locally while leveraging Rhino Federated Computing and @nvidia FLARE to orchestrate the network.
Learn how we enable secure, collaborative AI development: https://t.co/62edoUN3Xw
How do we accelerate AI-driven cancer research without compromising patient privacy?
At CAIA, we keep sensitive clinical data secure behind cancer center firewalls using federated learning.
Our new blog breaks down this approach.
Update: DataVoyager, which we launched in Preview early this fall, is now available in Asta. 🎉
You can upload real datasets, ask complex research questions in natural language, & get back reproducible answers + visualizations. 🔍📊