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With $40M in Funding, Cancer AI Alliance Aims to Pool Data, Computing Resources to Boost Research

NEW YORK – Four US cancer centers have teamed up with engineering and technology providers to establish the Cancer AI Alliance (CAIA) to support the development of computational infrastructure for the use of artificial intelligence in cancer research and care.

Dana-Farber Cancer Institute, Fred Hutch Cancer Center, Memorial Sloan Kettering Cancer Center, and Johns Hopkins University's Sidney Kimmel Comprehensive Cancer Center have received more than $40 million in funding for the CAIA from Deloitte, AWS, Microsoft, Nvidia, and Slalom, which will also contribute their AI technology and expertise. Johns Hopkins' Whiting School of Engineering is joining the alliance in partnership with SKCCC to develop data-sharing and AI methods. Fred Hutch Cancer Center is leading the alliance and will serve as its coordinating center.

CAIA will provide shared infrastructure for the analysis of the large volumes of data held by the cancer centers and ensure compliance with regulatory requirements, including patient privacy laws. This data includes electronic health records, pathology images, medical images, and genome sequencing. The alliance hopes to extract from this data new insights into tumor biology, treatment resistance, and therapeutic targets. Members of the alliance also aim to identify trends in the data related to rare cancers and small populations.

The alliance will use a federated AI learning framework with each cancer center maintaining independent data and AI models. Results of their AI analyses will then be distributed among participating members without exposing the underlying raw data to enable additional analysis.

CAIA expects to add more members to the alliance in the future according to criteria to be established by a governing committee that includes the four cancer center members. Its goal is to accrue $1 billion in resources over time to support cancer innovation.

The alliance expects to be operational by the end of 2024 and anticipates its first findings by the end of 2025.