Trial of Artificial Intelligence for Chest Radiography
- Conditions
- PneumoniaLung Cancer
- Registration Number
- NCT06456203
- Lead Sponsor
- Duke-NUS Graduate Medical School
- Brief Summary
Randomized Clinical Trial of the impact of Chest radiograph AI-assisted triage and report generation upon clinical outcomes and an economic analysis of impact of AI decision support on radiology service delivery.
- Detailed Description
Randomized, prospective selection of patients. Control group involves radiologists reporting chest radiographs as per reference standard clinical workflow Intervention group involves radiologists assisted with AI reporting an AI-triaged worklist of chest radiographs using an AI report generation tool Clinical outcomes on patients are studied at pre-determined study endpoints, including time to discharge from the hospital and re-admission rates.
Economic analysis on cost-avoidance from man-hours saved from report generation and triage.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 10000
- All patients attending radiography to have chest radiographs during the study period
- age below 14
- deceased before discharge
- chest radiograph performed in non-standard projections
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Primary Outcome Measures
Name Time Method Report generation time 12 months Time for radiologist to produce each individual CXR report
Turnaround Time 12 months Time from patient arrival at radiography department to time for clinical team to receive report
Time to discharge 12 months Time from patient arrival at radiography department to time to discharge from hospital
- Secondary Outcome Measures
Name Time Method 30-day patient readmission rate 12 months Rate of readmission of patient to hospital after discharge within 30 days