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NCT Number: NCT06934239

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.

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Key information

Conditions

Breast Cancer Screening Artificial Intelligence (AI)

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Phase 4

Primary location

University of California Los Angeles Health System, Los Angeles, California, United States

Loading trial locations.

About this study

During the RCT the AI support tool will be randomized to be turned on or off (1:1) at the mammography exam level. Patients who return for screening exams in year 2 of recruitment will be randomized again (e.g., they will not retain their prior randomization). Radiologists will not be able to sort exams based on AI availability or AI scores. Randomizing by exam level will ensure that we capture a substantial number of interpretations with vs. without AI for each radiologist, allowing for quantification of the radiologist-level AI learning curve. We are not randomizing at the facility level as some radiologists interpret exams acquired at different facilities on the same day. By randomizing AI at the exam level, we will have the best ability to estimate and adjust for temporal trends in screening outcomes across individual radiologists. Randomization across large regional health systems will be managed independently at each participating site.

Our RCT randomizes screening mammography exams to be interpreted either with or without an AI decision-support tool. As a result, radiologists cannot be blinded to study arm during screening mammography interpretation. However, interpreting radiologists and facility staff (e.g., those scheduling the exams) will not know in advance which patients will be randomized to the AI tool. Randomization occurs within minutes after the breast imaging acquisition (i.e., when the mammography technologist captures the images) by an automated system that was developed by a third-party AI platform and successfully piloted at UCLA. Thus, the AI data (or lack thereof) is embedded within the mammogram before the radiologist opens the exam, preventing any option to "add AI" to an exam randomized to be interpreted without AI. Radiologists will be aware of AI availability only at the time of interpretation, as AI information will appear upon opening the exam (e.g., the AI information pops up with the exam images).

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

  • Be at least 18 years of age or older
  • 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

  • Patients who have opted out of all research at the health system

Treatment and study plan

Artificial intelligence (AI) decision-support tool

Device

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 outcomes

  1. Cancer detection rate

    Time frame: Cancer diagnosed within 90 days of positive study entry screening mammogram

    Number of screening exams recommended for breast biopsy (final Breast Imaging- Reporting and Data System [BI-RADS] assessment of 4 or 5) resulting in detected cancer, per 1,000 screening exams

  2. Recall rate

    Time frame: Through study completion, an average of 1 year

    Number of screening exams recalled for diagnostic work-up (initial BI-RADS assessment of 0, 3, 4, or 5), per 1,000 screening exams

Secondary outcomes

  1. Interval cancer rate (i.e., false-negative rate)

    Time frame: Cancer diagnosed within 365 days of a negative study entry screening mammogram

    Number of screening exams with a negative assessment (final BI-RADS assessment of 1 or 2) and breast cancer diagnosed within 1 year, per 1,000 screening exams

  2. False positive recall rate

    Time frame: No cancer diagnosed within 365 days of a positive study entry screening mammogram

    Proportion of screening exams recalled for additional imaging (final BI-RADS assessment of 1, 2, or 3), with no breast cancer diagnosed within 1 year

  3. False positive short-interval follow-up recommendation rate

    Time frame: No cancer diagnosed within 365 days of a positive study entry screening mammogram

    Proportion of screening exams recalled for short-interval follow-up (final BI-RADS assessment of 3) with no breast cancer diagnosed within 1 year

  4. False positive biopsy recommendation rate

    Time frame: No cancer diagnosed within 365 days of a positive study entry screening mammogram

    Proportion of screening exams recalled for breast biopsy (final BI-RADS assessment of 4 or 5) with no breast cancer diagnosed within 1 year

  5. Trust and confidence in AI

    Time frame: Years 1,2 and Years 4,5

    Trust and confidence in AI gathered from focus group and survey data

  6. Efficiency metrics (only for the UCLA site)

    Time frame: Through study completion, an average of 1 year

    Interpretation time required for radiologists to interpret each mammogram with versus without AI.

    Delivery time, using time stamp data from exam acquisition to delivery of results to patients (aka turnaround time).

Study contacts

Contact information is provided by the study sponsor or research team.

Michelle L'Hommedieu, PhD

CONTACT

[email protected]

(310) 592-9454

Sponsors and collaborators

Lead sponsor

Jonsson Comprehensive Cancer Center

Other

Collaborators

  • Boston Medical Center
  • California Breast Cancer Research Program
  • Patient-Centered Outcomes Research Institute
  • University of California, Davis
  • University of California, Los Angeles
  • University of California, San Diego
  • University of Miami
  • University of Washington
  • University of Wisconsin, Madison

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2028
Study completion
2030
First posted
Apr 18, 2025
Registry last updated
Nov 26, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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