Johns Hopkins oncology specialists have joined forces with the Fred Hutchinson Cancer Center, Dana-Farber Cancer Institute and Memorial Sloan Kettering Cancer Center for a new national Cancer AI Alliance (CAIA) that brings together team science and vast data resources.

As part of CAIA, scientists from Johns Hopkins’ Kimmel Cancer Center and Whiting School of Engineering will develop projects centered on using artificial intelligence to enable precision cancer care, building better approaches to detect, intercept and treat cancers, considering each patient’s unique history and treatment path.

“Advances in measurement technologies, data science and AI have the potential to fundamentally transform cancer research and care for the benefit of our patients,” says Srinivasan Yegnasubramanian, an oncologist at the Kimmel Cancer Center and director of the inHealth Precision Medicine program at Johns Hopkins Medicine.

“To fully realize that potential, we must bring together interdisciplinary teams across many domains. Likewise, developing the large-scale, comprehensive and representative datasets that can fuel this AI-enabled transformation will be facilitated by bringing together the leading cancer centers partnering in this unique alliance.”

CAIA will provide the computing infrastructure to members of the alliance to process high volumes of cancer data generated during routine cancer care, such as electronic health records, pathology images, medical images and genome sequencing. These data, when paired with AI, could lead to novel insights about tumor biology, treatment resistance and identification of new therapeutic targets, leaders say.

The Fred Hutchinson Cancer Center, which spearheaded the formation and initial funding of CAIA (from AI technology leaders AWS, Deloitte, Microsoft and NVIDIA), will serve as the alliance’s coordinating center.

“This alliance has the potential to rapidly accelerate innovation in cancer care using AI,” says Alexis Battle, interim co-director of the Data Science and AI Institute and director of the Malone Center for Engineering in Healthcare at the Whiting School of Engineering.

“Leveraging data across multiple centers will foster intellectual collaboration, allow us to train more powerful models and, critically, ensure that AI methods are effective for diverse patient populations and treatment settings.”