Clinical trial · Interventional
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cancer | Malignant Neoplasm | ALIAS | 0.90 |
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- OTHER
- label
- Providers
- description
- Radiation oncology providers engaged in peer-review at participating clinics.
- interventionNames
- Device: The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning
- type
- NO_INTERVENTION
- label
- Patients
- description
- Prostate cancer patients who receive radiation therapy contribute de-identified safety outcomes.
Primary outcomes (1)
- measure
- Percentage of patients with changes nodal volume contours
- timeFrame
- Baseline
- description
- Percentage of patients with documented changes regarding nodal volume contours after Artificial Intelligence (AI) enhanced peer review.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
In order to participate in this study a subject must meet all of the eligibility criteria outlined below. Inclusion Criteria: Providers only * ≥18 years * Peer-review attendees at participating clinics Patients only * ≥18 years * All patients with prostate cancer radiation therapy cases treated at participating sites (no intervention delivered to patients) Exclusion Criteria: Providers only • Providers unwilling/unable to comply with study procedures; sites unable to implement the workflow or provide required outcomes. Patients and Providers • Has dementia, altered mental status, or any psychiatric or co-morbid condition prohibiting the understanding or rendering of informed consent
References
Publications (0)
Data not yet available