Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology

Part of paid clinical trials in Chapel Hill, North Carolina.

Sponsor
UNC Lineberger Comprehensive Cancer Center
Study ID
NCT07463833
Status
Not Yet Recruiting

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Conditions

Eligibility Criteria

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

Interventions

  • The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning — DEVICE
    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 / 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.

Study Details

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. The 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.

Key Dates

Start date
Jul 31, 2026
Status verified
Apr 2026
Primary completion
Jul 31, 2027
Completion
Jul 31, 2027

Study Design

Enrollment
207 participants (estimated)
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH

Arms

  • Other: Providers
    Radiation oncology providers engaged in peer-review at participating clinics.
  • No Intervention: Patients
    Prostate cancer patients who receive radiation therapy contribute de-identified safety outcomes.

Primary Outcome Measure

Percentage of patients with changes nodal volume contours [ Time Frame: Baseline ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
University of North Carolina at Chapel Hill, Department of Radiation OncologyChapel HillNorth Carolina27599
Olivia Morton
[email protected]
984-974-8441
Lukasz Mazur, PhD (PRINCIPAL_INVESTIGATOR)

Find similar trials in Chapel Hill, NC

By condition
Prostate cancer
By specialty
OncologyProstate oncologyMen's healthUrology
By research site
University of North Carolina at Chapel Hill, Department of Radiation Oncology· Chapel Hill, NC

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