Breast Cancer ScreeningPhase 4NCT06934239
A Trial Comparing Screening Mammography With and Without Assistance From Artificial Intelligence for Breast Cancer Detection and Recall Rates in Adult Patients
A Phase 4 study testing Artificial intelligence (AI) decision-support tool in people with Breast Cancer Screening and Artificial Intelligence (AI), run by Jonsson Comprehensive Cancer Center at 6 sites in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Breast Cancer Screening, Artificial Intelligence (AI)
- What is being tested?
- Artificial intelligence (AI) decision-support tool
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 400,000 participants
- Where
- 6 sitesUnited States
- Study dates
- 15 Oct 2025to 1 Mar 2030
- Phase
- Phase 4
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 1. Patients who have opted out of all research at the health system
Trial locations and contacts
6 sites in 1 country. Contact the study team or a site near you to ask about taking part.
6 sites in 1 country
United States6 sites, 6 recruiting
- Recruiting
Boston Medical Center
Boston, Massachusetts, 02118
Clare Poynton, MD, PhD, (617) 638-6626, clare.poynton@bmc.org
Clare Poynton, MD, PhD
- Recruiting
University of California Los Angeles Health System
Los Angeles, California, 90024
Michelle L'Hommedieu, PhD, (310) 592-9454, mlhommedieu@mednet.ucla.edu
Hannah S. Milch, MD
- Recruiting
University of Wisconsin-Madison
Madison, Wisconsin, 53706
Christoph Lee, MD, MSc, (608) 263-9377, cilee3@uwhealth.org
Christoph Lee, MD, MSc
- Recruiting
University of Miami Health System
Miami, Florida, 33136
Jose Net, MD, (305) 215-0461, jnet@med.miami.edu
Jose Net, MD
- Recruiting
University of California, San Diego
San Diego, California, 92093
Haydee Ojeda-Fournier, MD, (858) 442-1902, hojeda@health.ucsd.edu
Haydee Ojeda-Fournier, MD
- Recruiting
University of Washington Health System
Seattle, Washington, 98195
Janie Lee, MD, MSc, (206) 606-6241, jmlee58@fredhutch.org
Janie Lee, MD, MSc
About this study
Official 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
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?
Read more
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.
Read the detailed description
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).
- Being tested
- Artificial intelligence (AI) decision-support tool
- Keywords
- Breast cancer screening, Artificial intelligence (AI)
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence (AI) decision-support toolDevice
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 group | Role | What they receive |
|---|---|---|
| Intervention (radiologist assisted by AI) | Active comparator | 3D screening exams randomized to this arm will be interpreted by the radiologist assisted by the AI decision-support tool (i.e., intervention). Receives: Device: Artificial intelligence (AI) decision-support tool |
| Standard care (radiologist alone) | No intervention | 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). |
What is being measured
The primary outcome is the main question the study is designed to answer.
Cancer detection rate
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
Measured: Cancer diagnosed within 90 days of positive study entry screening mammogram
Recall rate
Number of screening exams recalled for diagnostic work-up (initial BI-RADS assessment of 0, 3, 4, or 5), per 1,000 screening exams
Measured: Through study completion, an average of 1 year
Secondary outcomes (6)
Interval cancer rate (i.e., false-negative rate)
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
Measured: Cancer diagnosed within 365 days of a negative study entry screening mammogram
False positive recall rate
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
Measured: No cancer diagnosed within 365 days of a positive study entry screening mammogram
False positive short-interval follow-up recommendation rate
Proportion of screening exams recalled for short-interval follow-up (final BI-RADS assessment of 3) with no breast cancer diagnosed within 1 year
Measured: No cancer diagnosed within 365 days of a positive study entry screening mammogram
False positive biopsy recommendation rate
Proportion of screening exams recalled for breast biopsy (final BI-RADS assessment of 4 or 5) with no breast cancer diagnosed within 1 year
Measured: No cancer diagnosed within 365 days of a positive study entry screening mammogram
Trust and confidence in AI
Trust and confidence in AI gathered from focus group and survey data
Measured: Years 1,2 and Years 4,5
Efficiency metrics (only for the UCLA site)
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).
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Apr 2025
- Study start15 Oct 2025
- Primary completion1 Mar 2028 (estimated)
- Study completion1 Mar 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast Cancer ScreeningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase 4
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- This is a study of an FDA-cleared artificial intelligence (AI) decision-support tool.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 400,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Jonsson Comprehensive Cancer Center
- Sponsor type
- University, hospital or other
- 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
- Responsible party
- Sponsor
- Investigators
- Joann G Elmore, MD, MPH, Principal investigator, University of California, Los AngelesDiana Miglioretti, PhD, Principal investigator, University of California, Davis
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06934239
- Sponsor's protocol ID
- 24-1192
- Status last verified
- Oct 2025
- First submitted
- 11 Apr 2025
- Last updated
- 26 Nov 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Yes: A de-identified dataset from this study will be deposited in the Patient-Centered Outcomes Data Repository (PCODR) housed at the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan, in compliance with PCORI's Policy on Data Management and Data Sharing.
Study at a glance
From the registry record, last updated by the sponsor on 26 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official 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
- ClinicalTrials.gov ID
- NCT06934239
- Acronym / sponsor ID
- 24-1192
- Keywords
- Breast cancer screening, Artificial intelligence (AI)
- Phase
- Phase 4
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 400,000 participants (estimated)
- Lead sponsor
- Jonsson Comprehensive Cancer CenterUniversity, hospital or other
- 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
- Study start
- 15 Oct 2025 (actual)
- Primary completion
- 1 Mar 2028 (estimated)
- Study completion
- 1 Mar 2030 (estimated)
- First posted
- 18 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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).
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- This is a study of an FDA-cleared artificial intelligence (AI) decision-support tool.
| Arm | Type | Receives |
|---|---|---|
| Intervention (radiologist assisted by AI) | Active comparator | Device: Artificial intelligence (AI) decision-support tool3D screening exams randomized to this arm will be interpreted by the radiologist assisted by the AI decision-support tool (i.e., intervention). |
| Standard care (radiologist alone) | No intervention | –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). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (1 listed)
- 1. Patients who have opted out of all research at the health system
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence (AI) decision-support toolThe 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. | Device | Intervention (radiologist assisted by AI) |
Primary outcome
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
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
6 secondary outcomes
Interval cancer rate (i.e., false-negative rate)
Time frame: Cancer diagnosed within 365 days of a negative study entry screening mammogram
False positive recall rate
Time frame: No cancer diagnosed within 365 days of a positive study entry screening mammogram
False positive short-interval follow-up recommendation rate
Time frame: No cancer diagnosed within 365 days of a positive study entry screening mammogram
False positive biopsy recommendation rate
Time frame: No cancer diagnosed within 365 days of a positive study entry screening mammogram
Trust and confidence in AI
Time frame: Years 1,2 and Years 4,5
Efficiency metrics (only for the UCLA site)
Time frame: Through study completion, an average of 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Nov 2025.
- Individual participant dataSharing planned: Yes. A de-identified dataset from this study will be deposited in the Patient-Centered Outcomes Data Repository (PCODR) housed at the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan, in compliance with PCORI's Policy on Data Management and Data Sharing.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Joann G Elmore, MD, MPH principal investigator
University of California, Los Angeles
Diana Miglioretti, PhD principal investigator
University of California, Davis
6 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A Phase 4 study testing Artificial intelligence (AI) decision-support tool in people with Breast Cancer Screening and Artificial Intelligence (AI), run by Jonsson Comprehensive Cancer Center at 6 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 26 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
What does this phase mean?
Phase 4 studies take place after a treatment is approved, to learn more about its long-term benefits and risks in everyday use.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Michelle L'Hommedieu, PhD ((310) 592-9454, mlhommedieu@mednet.ucla.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Cancer detection rate (time frame: Cancer diagnosed within 90 days of positive study entry screening mammogram). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Nov 2025.