Bridging the Gap: Integrating Clinical Knowledge, Simulation, and Generative AI for Lung Cancer Diagnosis

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to late-stage detection and the complexities of accurate diagnosis. Recent advances in artificial intelligence (AI) and medical imaging have created new avenues for improving early detection and diagnostic accuracy.This dissertation explores how clinical knowledge and virtual imaging trials can be integrated to enhance AI-driven lung cancer diagnosis. The research leverages weakly supervised deep learning models trained on large-scale body CT datasets, linking textual radiology reports with corresponding image data. By incorporating simulated imaging environments, known as virtual imaging trials, the work systematically evaluates algorithmic performance under controlled but realistic conditions. This approach not only bridges the gap between experimental validation and clinical applicability but also ensures reproducibility and standardization. The results demonstrate that incorporating domain-specific priors and synthetic datasets significantly improves detection sensitivity while reducing false positives. Furthermore, the investigation clarifies how representational biases in both data and model training can be mitigated through hybrid strategies that reflect authentic clinical decision-making. Overall, the dissertation presents a novel framework that unites computational modeling and radiological expertise, providing a robust foundation for the next generation of AI systems in medical imaging. The findings have broad implications for translational healthcare, regulatory assessment of AI tools, and clinical adoption pathways.