A Trial Comparing Screening Mammography With and Without Assistance From Artificial Intelligence for Breast Cancer Detection and Recall Rates in Adult Patients

The goal of this clinical trial is to compare patient-centered outcomes when screening digital breast tomosynthesis (DBT) exams are interpreted with versus without a leading FDA-cleared artificial intelligence (AI) decision-support tool in real-world U.S. settings and to assess patients' and radiologists' perspectives on AI in medicine. The main question it aims to answer is: Does an FDA-cleared AI decision-support tool for digital tomosynthesis (DBT) improve screening outcomes in real world US clinical settings? This trial will include all interpreting radiologists and all adult patients undergoing screening mammography at any of the participating breast imaging facilities across 6 regional health systems (University of California, Los Angeles (UCLA), University of California, San Diego (UCSD), University of Washington-Seattle, University of Wisconsin-Madison, Boston Medical Center, and University of Miami) during the trial period. All screening mammograms at these facilities will be randomized to either intervention (radiologist assisted by an AI decision support tool) versus usual care (radiologist alone) to see if interpreting these mammograms with the AI tool's assistance improves patient screening outcomes. We are targeting 400,000 screening exams across the participating health systems in this trial.

NCT Number	NCT06934239
Lead Sponsor	Jonsson Comprehensive Cancer Center Collaborators: University of California, Los Angeles, University of California, San Diego, University of Wisconsin, Madison, Boston Medical Center, Patient-Centered Outcomes Research Institute, University of Washington, California Breast Cancer Research Program, University of Miami, University of California, Davis
Conditions	Breast Cancer Screening, Artificial Intelligence (AI)
Enrollment	400,000 participants
Start Date	2025-10-15
Primary Completion	2028-03-01 (estimated)
Study Completion	2030-03-01 (estimated)
Updated on ClinicalTrials.gov	2025-11-26