[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100587888":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":25,"centralContacts":33,"locations":39,"responsibleParty":138,"collaborators":140,"id":155,"slug":156,"hasResults":157,"nctId":158,"briefTitle":159,"officialTitle":160,"acronym":18,"eligibilityCriteria":161,"healthyVolunteers":162,"sex":163,"minAge":164,"maxAge":18,"enrollmentInfo":165,"targetDuration":18,"studyType":168,"phases":169,"briefSummary":171,"conditions":172,"keywords":175,"overallStatus":42,"whyStopped":18,"lastUpdateSubmitDate":178,"lastUpdatePostDateStruct":179,"startDateStruct":182,"completionDateStruct":184,"leadSponsor":186,"locationsCount":187},{"fullName":5,"class":6},"Jonsson Comprehensive Cancer Center","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"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).",[13],"Device: Artificial intelligence (AI) decision-support tool",{"label":15,"type":16,"description":17,"interventionNames":18},"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).",null,[20],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":18},"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\u002Foff 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).\n\nRandomization 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.",[9],[26,30],{"name":27,"affiliation":28,"role":29},"Joann G Elmore, MD, MPH","University of California, Los Angeles","PRINCIPAL_INVESTIGATOR",{"name":31,"affiliation":32,"role":29},"Diana Miglioretti, PhD","University of California, Davis",[34],{"name":35,"role":36,"phone":37,"phoneExt":18,"email":38},"Michelle L'Hommedieu, PhD","CONTACT","(310) 592-9454","mlhommedieu@mednet.ucla.edu",[40,59,74,90,106,122],{"facility":41,"status":42,"city":43,"state":44,"zip":45,"country":46,"countryCode":47,"cosmosGeoPoint":48,"geoPoint":53,"contacts":54},"University of California Los Angeles Health System","RECRUITING","Los Angeles","California","90024","United States","US",{"type":49,"coordinates":50},"Point",[51,52],-118.24368,34.05223,{"lat":52,"lon":51},[55,56,58],{"name":35,"role":36,"phone":37,"phoneExt":18,"email":38},{"name":57,"role":29,"phone":18,"phoneExt":18,"email":18},"Hannah S. Milch, MD",{"name":27,"role":29,"phone":18,"phoneExt":18,"email":18},{"facility":60,"status":42,"city":61,"state":44,"zip":62,"country":46,"countryCode":47,"cosmosGeoPoint":63,"geoPoint":67,"contacts":68},"University of California, San Diego","San Diego","92093",{"type":49,"coordinates":64},[65,66],-117.16472,32.71571,{"lat":66,"lon":65},[69,73],{"name":70,"role":36,"phone":71,"phoneExt":18,"email":72},"Haydee Ojeda-Fournier, MD","(858) 442-1902","hojeda@health.ucsd.edu",{"name":70,"role":29,"phone":18,"phoneExt":18,"email":18},{"facility":75,"status":42,"city":76,"state":77,"zip":78,"country":46,"countryCode":47,"cosmosGeoPoint":79,"geoPoint":83,"contacts":84},"University of Miami Health System","Miami","Florida","33136",{"type":49,"coordinates":80},[81,82],-80.19366,25.77427,{"lat":82,"lon":81},[85,89],{"name":86,"role":36,"phone":87,"phoneExt":18,"email":88},"Jose Net, MD","(305) 215-0461","jnet@med.miami.edu",{"name":86,"role":29,"phone":18,"phoneExt":18,"email":18},{"facility":91,"status":42,"city":92,"state":93,"zip":94,"country":46,"countryCode":47,"cosmosGeoPoint":95,"geoPoint":99,"contacts":100},"Boston Medical Center","Boston","Massachusetts","02118",{"type":49,"coordinates":96},[97,98],-71.05977,42.35843,{"lat":98,"lon":97},[101,105],{"name":102,"role":36,"phone":103,"phoneExt":18,"email":104},"Clare Poynton, MD, PhD","(617) 638-6626","clare.poynton@bmc.org",{"name":102,"role":29,"phone":18,"phoneExt":18,"email":18},{"facility":107,"status":42,"city":108,"state":109,"zip":110,"country":46,"countryCode":47,"cosmosGeoPoint":111,"geoPoint":115,"contacts":116},"University of Washington Health System","Seattle","Washington","98195",{"type":49,"coordinates":112},[113,114],-122.33207,47.60621,{"lat":114,"lon":113},[117,121],{"name":118,"role":36,"phone":119,"phoneExt":18,"email":120},"Janie Lee, MD, MSc","(206) 606-6241","jmlee58@fredhutch.org",{"name":118,"role":29,"phone":18,"phoneExt":18,"email":18},{"facility":123,"status":42,"city":124,"state":125,"zip":126,"country":46,"countryCode":47,"cosmosGeoPoint":127,"geoPoint":131,"contacts":132},"University of Wisconsin-Madison","Madison","Wisconsin","53706",{"type":49,"coordinates":128},[129,130],-89.40123,43.07305,{"lat":130,"lon":129},[133,137],{"name":134,"role":36,"phone":135,"phoneExt":18,"email":136},"Christoph Lee, MD, MSc","(608) 263-9377","cilee3@uwhealth.org",{"name":134,"role":29,"phone":18,"phoneExt":18,"email":18},{"type":139,"investigatorFullName":18,"investigatorTitle":18,"investigatorAffiliation":18,"oldNameTitle":18,"oldOrganization":18},"SPONSOR",[141,142,143,145,146,148,150,152,154],{"name":28,"class":6},{"name":60,"class":6},{"name":144,"class":6},"University of Wisconsin, Madison",{"name":91,"class":6},{"name":147,"class":6},"Patient-Centered Outcomes Research Institute",{"name":149,"class":6},"University of Washington",{"name":151,"class":6},"California Breast Cancer Research Program",{"name":153,"class":6},"University of Miami",{"name":32,"class":6},"100587888","phase-4-a-trial-comparing-screening-mammography-with-and-without-assistance-from-artificial-intelligence-for-breast-cancer-detection-and-recall-rates-in-adult-patients-100587888",false,"NCT06934239","A Trial Comparing Screening Mammography With and Without Assistance From Artificial Intelligence for Breast Cancer Detection and Recall Rates in Adult Patients","A Randomized Controlled Trial Comparing Screening Mammography With and Without Assistance From Artificial Intelligence for Breast Cancer Detection and Recall Rates in Adult Patients","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.\n\nInclusion Criteria:\n\n1. Be at least 18 years of age or older\n2. 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.\n\nExclusion Criteria:\n\n1\\. Patients who have opted out of all research at the health system",true,"ALL","18 Years",{"count":166,"type":167},400000,"ESTIMATED","INTERVENTIONAL",[170],"PHASE4","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.\n\nThe 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?\n\nThis 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.\n\nAll 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.\n\nWe are targeting 400,000 screening exams across the participating health systems in this trial.",[173,174],"Breast Cancer Screening","Artificial Intelligence (AI)",[176,177],"Breast cancer screening","Artificial intelligence (AI)","2025-11-24",{"date":180,"type":181},"2025-11-26","ACTUAL",{"date":183,"type":181},"2025-10-15",{"date":185,"type":167},"2030-03-01",{"name":5,"class":6},6]