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

Part of paid clinical trials in Los Angeles, California.

Sponsor
Jonsson Comprehensive Cancer Center
Study ID
NCT06934239
Phase
PHASE4
Status
Recruiting
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Conditions

  • Artificial Intelligence (AI)
  • Breast Cancer Screening

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Accepted

Interventions

  • 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.

Study Details

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.

Key Dates

Start date
Oct 15, 2025
Status verified
Oct 2025
Primary completion
Mar 1, 2028
Completion
Mar 1, 2030

Study Design

Enrollment
400,000 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING

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).

Primary Outcome Measure

Cancer detection rate [ Time Frame: Cancer diagnosed within 90 days of positive study entry screening mammogram ]

Central Contacts

Locations (6)

FacilityCityStateZIPSite coordinators
University of California Los Angeles Health SystemLos AngelesCalifornia90024
Michelle L'Hommedieu, PhD
[email protected]
(310) 592-9454
Hannah S. Milch, MD (PRINCIPAL_INVESTIGATOR)
Joann G Elmore, MD, MPH (PRINCIPAL_INVESTIGATOR)
University of California, San DiegoSan DiegoCalifornia92093
Haydee Ojeda-Fournier, MD
[email protected]
(858) 442-1902
Haydee Ojeda-Fournier, MD (PRINCIPAL_INVESTIGATOR)
University of Miami Health SystemMiamiFlorida33136
Jose Net, MD
[email protected]
(305) 215-0461
Jose Net, MD (PRINCIPAL_INVESTIGATOR)
Boston Medical CenterBostonMassachusetts02118
Clare Poynton, MD, PhD
[email protected]
(617) 638-6626
Clare Poynton, MD, PhD (PRINCIPAL_INVESTIGATOR)
University of Washington Health SystemSeattleWashington98195
Janie Lee, MD, MSc
[email protected]
(206) 606-6241
Janie Lee, MD, MSc (PRINCIPAL_INVESTIGATOR)
University of Wisconsin-MadisonMadisonWisconsin53706
Christoph Lee, MD, MSc
[email protected]
(608) 263-9377
Christoph Lee, MD, MSc (PRINCIPAL_INVESTIGATOR)

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By research site
University of California Los Angeles Health System· Los Angeles, CAUniversity of California, San Diego· San Diego, CAUniversity of Miami Health System· Miami, FLBoston Medical Center· Boston, MAUniversity of Washington Health System· Seattle, WAUniversity of Wisconsin-Madison· Madison, WI

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