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.
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
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
Parallel
AI
Other interventionAn integrated AI-human collaborative workflow for lung cancer screening interpretation
Diagnostic Accuracy
Sensitivity and Specificity: Comparison of AI-assisted versus manual interpretation in detecting malignant pulmonary nodules, validated against histopathological confirmation or 12-month clinical follow-up. Early Detection Rate: Proportion of stage I/II lung cancers correctly identified by each method.
Interpretation Consistency
Inter-reader Agreement: Measured by Cohen's kappa (κ) between AI-assisted radiologists and the independent review committee (IRC). Intra-reader Variability: Consistency of nodule classification in repeat readings (subset analysis).
Cost-Effectiveness
Resource Utilization: Comparative analysis of staffing, equipment, and follow-up costs per detected cancer case. Incremental Cost per QALY (Quality-Adjusted Life Year): Long-term economic impact modeling.
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