Recruiting

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

Sponsor:

Jonsson Comprehensive Cancer Center

Code:

NCT06934239

Conditions

Breast Cancer Screening

Artificial Intelligence (AI)

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Accepted

Interventions

Artificial intelligence (AI) decision-support tool

Study Details

Brief 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)

Study ID

NCT06934239

Start date

Oct 15, 2025

Status verified date

Oct, 2025

Completion date

Mar 1, 2030

Anticipated

Primary completion date

Mar 1, 2028

Anticipated

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Accepted

This trial will include all radiologists interpreting screening mammography and all adult patients undergoing screening mammography at any of the participating breast imaging facilities across 6 regional health systems (UCLA, UC San Diego, University of Washington-Seattle, University of Wisconsin-Madison, Boston Medical Center, and University of Miami) during the trial period. Individuals must meet the following eligiblity criteria.

Inclusion Criteria:

1. Be at least 18 years of age or older
2. Receive a screening mammogram at one of the participating breast imaging facilities OR be a radiologist who interprets screening mammograms at one of the participating breast imaging facilities.

Exclusion Criteria:

1\. Patients who have opted out of all research at the health system

Study Design

Enrollment

400000 participants

Anticipated

Allocation

Randomized

Intervention Model

Parallel Assignment

Primary purpose

Screening

Interventions and Outcome Measures

Arms

active comparator: Intervention (radiologist assisted by AI)

3D screening exams randomized to this arm will be interpreted by the radiologist assisted by the AI decision-support tool (i.e., intervention).

no intervention: Standard care (radiologist alone)

3D screening exams randomized to this arm will be interpreted in accordance with standard care (i.e., interpreted by the radiologist alone, without an AI decision-support tool's assistance).

Interventions

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.

Primary outcome measure

Cancer detection rate [ Time Frame: Cancer diagnosed within 90 days of positive study entry screening mammogram ]
Recall rate [ Time Frame: Through study completion, an average of 1 year ]

Central Contacts and Locations

Central contacts

Michelle L'Hommedieu, PhD

(310) 592-9454 mlhommedieu@mednet.ucla.edu

Locations

University of California Los Angeles Health System

Recruiting

Los Angeles, California, United States, 90024

Contacts

Michelle L'Hommedieu, PhD

(310) 592-9454 mlhommedieu@mednet.ucla.edu

Principal Investigator:

Hannah S. Milch, MD

University of California, San Diego

Recruiting

San Diego, California, United States, 92093

Contacts

Haydee Ojeda-Fournier, MD

(858) 442-1902 hojeda@health.ucsd.edu

Principal Investigator:

Haydee Ojeda-Fournier, MD

University of Miami Health System

Recruiting

Miami, Florida, United States, 33136

Contacts

Jose Net, MD

(305) 215-0461 jnet@med.miami.edu

Principal Investigator:

Jose Net, MD

Boston Medical Center

Recruiting

Boston, Massachusetts, United States, 02118

Contacts

Clare Poynton, MD, PhD

(617) 638-6626 clare.poynton@bmc.org

Principal Investigator:

Clare Poynton, MD, PhD

University of Washington Health System

Recruiting

Seattle, Washington, United States, 98195

Contacts

Janie Lee, MD, MSc

(206) 606-6241 jmlee58@fredhutch.org

Principal Investigator:

Janie Lee, MD, MSc

University of Wisconsin-Madison

Recruiting

Madison, Wisconsin, United States, 53706

Contacts

Christoph Lee, MD, MSc

(608) 263-9377 cilee3@uwhealth.org

Principal Investigator:

Christoph Lee, MD, MSc

More Information

Sponsor

Jonsson Comprehensive Cancer Center

Last update posted

Nov 26, 2025

Last verified

Oct, 2025

Keywords

Breast cancer screening
Artificial intelligence (AI)

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-05. This information was provided to ClinicalTrials.gov by Jonsson Comprehensive Cancer Center on 2025-11-26.