Leveraging Large Language Models for Clinical Information Extraction in Radiology Reports

Abstract

Medical imaging plays a central role in diagnosing, monitoring, and managing a wide spectrum of diseases, including cancer, cardiovascular disorders, neurological conditions, and musculoskeletal abnormalities. Radiologists interpret complex imaging data and summarize their findings in narrative reports, which remain largely unstructured. The rapid expansion of imaging utilization has led to an overwhelming volume of such reports, posing significant challenges for clinical decision support. Their unstructured format limits automated analysis, secondary use, and integration into downstream clinical workflows. This dissertation addresses two major barriers to the effective use of radiology reports in data-driven clinical systems: the absence of publicly available, large-scale annotated corpora of radiology reports with detailed clinical findings suitable for training supervised models, and the limited application of machine learning approaches, particularly large language models (LLMs), to real-world clinical tasks at scale. To overcome these challenges, the research is organized around three core aims: developing a corpus of radiology reports annotated with detailed clinical findings and designing an advanced information extraction framework optimized for radiologic text; evaluating the performance of diverse machine learning approaches, with emphasis on LLMs, for the practical task of identifying follow-up imaging recommendations; and constructing a large-scale repository of incidental findings (incidentalomas) derived from the model outputs and proposing an NLP-based framework for automated incidentaloma detection to enhance clinical decision-making. Collectively, this work contributes a high-quality annotated dataset for radiologic text analysis and demonstrates the feasibility and utility of large language model approaches for transforming unstructured radiology reports into structured clinical intelligence, advancing the integration of medical imaging data into precision healthcare.