Ai2 and Providence Swedish Cancer Institute partner to advance AI-assisted scientific discovery

Summary

Ai2 and Providence Swedish Cancer Institute partner to advance AI-assisted scientific discovery The collaboration includes clinically meaningful research findings on lobular breast cancer, and a local deployment to enable next-gen discovery while keeping clinical data secure. This partnership represents an important milestone for AutoDiscovery as it moves beyond public research datasets into active cancer research programs at a leading oncology research center. It’s a particularly significant deployment given the high standard required for AI adoption in biomedical science, where every finding must be transparent, reproducible, and independently validated before informing future research. The decision to expand the collaboration builds on a new research paperauthored by a joint team led by Providence researchers, Dr. Kelly Paulson of the Paul G. Allen Research Center and Dr. Sasha Stanton of the Earle A. Chiles Research Institute, and Ai2’s Senior Research Scientist Bodhisattwa Majumder. Working together, the team used AutoDiscovery to analyze one of the world's most extensively studied breast cancer datasets, uncovering a stronger immune signature in invasive lobular breast cancer than previously recognized. Subsequently, this finding was validated across an independent patient dataset and through laboratory analysis. The findings suggest that it may be possible for an entire class of breast cancer patients to benefit from immunotherapy, representing roughly 15% of breast cancers diagnosed in the US each year. The results also illustrate how AI-assisted hypothesis generation can help researchers uncover questions worthy of further investigation. Helping researchers navigate an era of scientific abundance Biomedical research has entered an era of unprecedented data generation. Public repositories now contain decades of genomic, molecular, clinical, and imaging data representing millions of patients, and protected datasets within cancer centers and research institutes represent a potential trove of new insights. While these resources have transformed medicine, they have also created a new challenge: the scale and complexity of modern datasets make it increasingly difficult to comprehensively explore the scientific questions they contain. Scientific research has traditionally been driven by hypothesis testing. Researchers develop a question, design an experim…