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UCLA and UC Davis will co-lead a newly funded, multi-institutional clinical trial to evaluate whether artificial intelligence (AI) can help support radiologists in interpreting mammograms more accurately, with the goal of improving breast cancer screening and reducing unnecessary callbacks and anxiety for patients.

The study, known as the PRISM Trial (Pragmatic Randomized Trial of Artificial Intelligence for Screening Mammography), is supported by a $16 million award from the Patient-Centered Outcomes Research Institute (PCORI). The study will involve hundreds of thousands of mammograms interpreted at academic medical centers and breast imaging facilities in California, Florida, Massachusetts, Washington and Wisconsin. The AI support tool studied will be Transpara by ScreenPoint Medical with clinical workflow integration provided by the Aidoc aiOS platform.

“This is the first large-scale randomized trial of AI in breast cancer screening in the United States,” said Dr. Joann G. Elmore, Dual Principal Investigator and lead of the Administrative Coordinating Site, and professor of medicine at the David Geffen School of Medicine at UCLA and of health policy and management at the UCLA Fielding School of Public Health. “We’re looking carefully and objectively at whether AI helps or hinders — and for whom. Expert radiologists remain in the driver’s seat for all interpretations.”

A pressing question in breast cancer care
Breast cancer remains one of the leading causes of cancer death among women in the U.S. While routine mammography screening reduces mortality through early detection, it also has drawbacks — including false positives that can lead to unnecessary testing, anxiety and costs, and missed cancers.

“AI has great promise, but it also raises real questions,” said Elmore, who is also an investigator at the UCLA Health Jonsson Comprehensive Cancer Center and serves as director of the UCLA National Clinician Scholars Program. “We want to know whether AI helps radiologists find more cancers, or just flags more exams that ultimately turn out to be normal.”