The Impact of AI Assistance on Radiologist Performance and Healthcare Costs in LDCT-Based Lung Cancer Screening
Evaluation of AI-Assisted Versus Conventional Human Reading for Lung Cancer Screening in Community-Based Settings: A Randomized Controlled Trial
The First Affiliated Hospital of Guangzhou Medical University
7,294 participants
Jul 1, 2024
INTERVENTIONAL
Conditions
Summary
AI diagnostic systems show great promise for improving lung cancer screening in community healthcare settings. While not originally designed for primary care, these tools demonstrate capabilities in nodule detection and workflow optimization. However, their effectiveness in resource-limited community centers requires thorough evaluation. This RCT compares AI-assisted versus manual CT interpretation across community health centers. Expert radiologists will establish reference standards, while an independent committee blindly evaluates cases from both groups. The study assesses diagnostic accuracy, operational efficiency, and cost-effectiveness, with blinded analysts resolving discrepancies through consensus to ensure reliable results.
Eligibility
Inclusion Criteria4
- Aged 45-74 years
- Permanent resident of participating study communities
- No prior history of lung cancer and no lung cancer screening within the past 3 months
- Able to comprehend and voluntarily sign informed consent, with willingness to participate in long-term follow-up
Exclusion Criteria5
- Individuals with a confirmed diagnosis of lung cancer
- Those with severe comorbidities contraindicating CT imaging
- Inability to understand study protocols or provide informed consent due to cognitive impairment
- Concurrent participation in other clinical trials that may interfere with study outcomes
- Unable to comply with follow-up requirements
Interventions
An integrated AI-human collaborative workflow for lung cancer screening interpretation
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT06988579