The Impact of AI Assistance on Radiologist Performance and Healthcare Costs in LDCT-Based Lung Cancer Screening

Condition(s) studied

Investigational drug(s) / intervention(s)

AI: An integrated AI-human collaborative workflow for lung cancer screening interpretation

Study 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

Primary outcome measure(s)

• Diagnostic Accuracy — One year after entry
  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 — One year after entry
  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).

Trial sites (1)

More The First Affiliated Hospital of Guangzhou Medical University trials in China

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