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

Today, Ai2 announced a new partnership with the Paul G. Allen Research Center (PARC) at Providence Swedish Cancer Institute to apply Ai2’s AI-powered scientific discovery platform, AutoDiscovery , across cancer research datasets.

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 paper authored 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.

Scientific research has traditionally been driven by hypothesis testing. Researchers develop a question, design an experiment, and evaluate the results. AutoDiscovery introduces a complementary approach to exploring complex datasets by generating and evaluating surprising hypotheses with large language models, which researchers can then investigate through established scientific methods. Rather than evaluating all possible directions equally, it prioritizes observations that are both surprising relative to prior expectations and reproducible across analyses, allowing iterative interrogation of the most informative signals.

Rather than operating independently, AutoDiscovery was designed to work alongside scientists, allowing researchers to guide it in promising directions, apply domain expertise, and determine which findings warrant further investigation. The collaboration between Ai2 and PARC demonstrated that this partnership between AI and researchers produced stronger results than either could achieve alone.

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.

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.

“AutoDiscovery wasn't built to replace scientific expertise. It was built to amplify it,” said Bodhisattwa Majumder , Senior Research Scientist, Ai2. “The most meaningful discoveries come from enhancing AI's ability to systematically explore complex datasets with the experience and intuition of scientists who know how to interpret, validate, and build on those findings. Our collaboration with the Paul G. Allen Research Center demonstrates what that model can achieve.”