[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100628607":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":26,"centralContacts":30,"locations":40,"responsibleParty":59,"collaborators":61,"id":65,"slug":66,"hasResults":67,"nctId":68,"briefTitle":69,"officialTitle":70,"acronym":17,"eligibilityCriteria":71,"healthyVolunteers":72,"sex":73,"minAge":74,"maxAge":17,"enrollmentInfo":75,"targetDuration":17,"studyType":78,"phases":79,"briefSummary":81,"conditions":82,"keywords":85,"overallStatus":43,"whyStopped":17,"lastUpdateSubmitDate":93,"lastUpdatePostDateStruct":94,"startDateStruct":97,"completionDateStruct":98,"leadSponsor":100,"locationsCount":101},{"fullName":5,"class":6},"UNC Lineberger Comprehensive Cancer Center","OTHER",[8,13],{"label":9,"type":6,"description":10,"interventionNames":11},"Providers","Radiation oncology providers engaged in peer-review at participating clinics.",[12],"Device: The Artificial Intelligence (AI)\u002F Machine Learning (ML) contribution to treatment planning",{"label":14,"type":15,"description":16,"interventionNames":17},"Patients","NO_INTERVENTION","Prostate cancer patients who receive radiation therapy contribute de-identified safety outcomes.",null,[19],{"type":20,"name":21,"description":22,"armGroupLabels":23,"otherNames":24},"DEVICE","The Artificial Intelligence (AI)\u002F Machine Learning (ML) contribution to treatment planning","All treatment planning and clinical monitoring are conducted in accordance with institutional standards and established departmental policies. Peer review activities proceed as they would in routine clinical practice, with the addition of optional Artificial Intelligence (AI) generated analytics available for clinician review. AI \u002F Machine Learning (ML) system is embedded in scheduled departmental peer review meetings and presents analytic summaries and visualizations through a dashboard that is integrated into the existing clinical workflow. The system functions solely as a decision support aid and does not perform or initiate any autonomous treatment planning actions, dose delivery changes, or clinical interventions. During simulation (SIM) review, physician generated target and organ at risk contours are reviewed first, consistent with standard practice. Only after this initial review may the treating physician optionally access the AI generated contours for comparative purposes.",[9],[25],"Clinical decision support \u002F workflow support",[27],{"name":28,"affiliation":5,"role":29},"Lukasz Mazur, PhD","PRINCIPAL_INVESTIGATOR",[31,36],{"name":32,"role":33,"phone":34,"phoneExt":17,"email":35},"Olivia Morton","CONTACT","(984) 974-8441","olivia_roberts@med.unc.edu",{"name":37,"role":33,"phone":38,"phoneExt":17,"email":39},"Victoria Xu","(984) 974-8444","victoria_xu@med.unc.edu",[41],{"facility":42,"status":43,"city":44,"state":45,"zip":46,"country":47,"countryCode":48,"cosmosGeoPoint":49,"geoPoint":54,"contacts":55},"University of North Carolina at Chapel Hill, Department of Radiation Oncology","RECRUITING","Chapel Hill","North Carolina","27599","United States","US",{"type":50,"coordinates":51},"Point",[52,53],-79.05584,35.9132,{"lat":53,"lon":52},[56,58],{"name":32,"role":33,"phone":57,"phoneExt":17,"email":35},"984-974-8441",{"name":28,"role":29,"phone":17,"phoneExt":17,"email":17},{"type":60,"investigatorFullName":17,"investigatorTitle":17,"investigatorAffiliation":17,"oldNameTitle":17,"oldOrganization":17},"SPONSOR",[62],{"name":63,"class":64},"Agency for Healthcare Research and Quality (AHRQ)","FED","100628607","artificial-intelligence-ai-enhanced-pretreatment-peer-review-process-to-improve-patient-safety-in-radiation-oncology-100628607",false,"NCT07463833","Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology","Development and Assessment of Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology","In order to participate in this study a subject must meet all of the eligibility criteria outlined below.\n\nInclusion Criteria:\n\nProviders only\n\n* ≥18 years\n* Peer-review attendees at participating clinics\n\nPatients only\n\n* ≥18 years\n* All patients with prostate cancer radiation therapy cases treated at participating sites (no intervention delivered to patients)\n\nExclusion Criteria:\n\nProviders only\n\n• Providers unwilling\u002Funable to comply with study procedures; sites unable to implement the workflow or provide required outcomes.\n\nPatients and Providers\n\n• Has dementia, altered mental status, or any psychiatric or co-morbid condition prohibiting the understanding or rendering of informed consent",true,"ALL","18 Years",{"count":76,"type":77},207,"ESTIMATED","INTERVENTIONAL",[80],"NA","This prospective study will test artificial intelligence (AI) and machine learning (ML) decision support tools. This tool is designed to help doctors, physicists and other staff during pre-treatment peer review, a step where treatment plans are checked before a patient begins care.\n\nThe system highlights summaries showing how different providers may vary in their treatment planning (provider-variability summaries) and points out the best signals or warning signs to look for (optimal cues). By drawing attention to these patterns and cues, the tool aims to help reviewers spot possible treatment-planning mistakes earlier, reduce the chance of errors, and improve overall patient safety.",[83,84],"Cancer","Prostate Cancer",[86,87,88,89,90,91,92],"radiation therapy","artificial intelligence (AI)","machine learning (ML)","radiation therapy (RT)","intensity-modulated radiation therapy (IMRT)","Volumetric Modulated Arc Therapy (VMAT)","Image-guided radiation therapy (IGRT)","2026-06-22",{"date":95,"type":96},"2026-06-23","ACTUAL",{"date":93,"type":96},{"date":99,"type":77},"2027-07",{"name":5,"class":6},1]