AI-Driven Detection of Early-Stage Lung Cancer in Chest CT Scans 1. Research Aim and Hypothesis Aim: To evaluate the effectiveness of an AI-based algorithm in detecting early-stage lung cancer from chest CT scans compared to traditional radiological assessment. Hypothesis: AI-based detection methods will have higher sensitivity and specificity in identifying early-stage lung cancer compared to standard radiologist interpretation. 2. Gaps in the Existing Literature Current research shows promise for AI in image analysis but lacks robust, randomized controlled trials (RCTs) comparing AI performance with radiologists in early-stage cancer detection. Limited studies evaluate real-world integration of AI in clinical workflows. 3. Research Design and Methodology Design: Randomized controlled trial comparing AI-assisted detection with standard radiologist interpretation. Methodology: Intervention group: Radiologists assisted by AI algorithms. Control group: Radiologists interpreting images without AI assistance. 4. Participant Recruitment and Sampling Selection criteria: Adult patients undergoing routine chest CT scans in tertiary hospitals. Sampling: Stratified random sampling to ensure representation of diverse demographics. Sample size: Estimated 500 patients, calculated based on power analysis to detect significant differences. 5. Data Collection and Analysis Methods Data collection: Annotated chest CT scans, AI predictions, and radiologists' interpretations. Analysis: Sensitivity, specificity, and diagnostic accuracy will be assessed using statistical tools such as ROC curve analysis. 6. Ethical and Legal Considerations Ethical approval from institutional review boards (IRBs). Informed consent from all participants. Data anonymization to comply with HIPAA/GDPR regulations. 7. Expected Contributions and Practical Applications Contribution to knowledge: Evidence for integrating AI in routine radiological practices. Practical applications: Development of guidelines for AI use in early-stage cancer detection.