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

Summary

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.

Conditions

• Breast Cancer Screening
• Artificial Intelligence (AI)

Interventions

DEVICE

Artificial intelligence (AI) decision-support tool

The intervention is an AI decision-support tool to help radiologists interpret 3D screening mammograms. For exams randomized to this intervention arm, the first image displayed to the radiologist upon opening an exam on the viewing station will be a one-page, standardized AI report showing the overall exam risk (elevated, intermediate, or low), image region markings, lesion scores from 1-100 (100 being the highest suspicion), bounding boxes, and relevant slice locations for 3D exams. Radiologists can toggle markings on/off and retain full control over the final interpretation of the exam as positive or negative (i.e., they can choose to ignore the AI information). Randomization occurs 1:1 at the exam level via automated code at image acquisition. Returning patients in year two will be re-randomized. Radiologists cannot filter their exam lists by AI availability or risk, and randomization will be independently managed at each participating health system.

Sponsors & Collaborators

• University of California, Los Angeles

  collaborator OTHER

• University of California, San Diego

  collaborator OTHER

• University of Wisconsin, Madison

  collaborator OTHER

• Boston Medical Center

  collaborator OTHER

• Patient-Centered Outcomes Research Institute

  collaborator OTHER

• University of Washington

  collaborator OTHER

• California Breast Cancer Research Program

  collaborator OTHER

• University of Miami

  collaborator OTHER

• University of California, Davis

  collaborator OTHER

• Jonsson Comprehensive Cancer Center

  lead OTHER

Principal Investigators

• Joann G Elmore, MD, MPH · University of California, Los Angeles

• Diana Miglioretti, PhD · University of California, Davis

Study Design

Allocation

RANDOMIZED

Purpose

SCREENING

Masking

SINGLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2025-10-15

Primary Completion

2028-03-01

Completion

2030-03-01

FDA Device

Yes

Countries

• United States

Study Locations