Optimizing Clinical Data Availability: Extracting Pulmonary Embolism Diagnoses from Radiology Impressions with GPT-4o

Abstract

Background: Pulmonary embolism (PE) is a life-threatening condition that requires timely diagnosis to reduce mortality. Radiology reports, particularly the Impression sections, play a critical role in diagnosing PE. However, manually extracting this information from large volumes of reports is challenging. This study aims to develop an advanced natural language processing (NLP) system using GPT-4o to automatically extract PE diagnoses from radiology report impressions, enhancing clinical workflows and decision-making. Materials and Methods: We developed two text classification models: a fine-tuned Clinical Longformer (as a baseline model) and GPT-4o. Models were trained using 1,000 radiology report impressions and validated on 200 samples, with a post-deployment evaluation conducted using 500 operational records. The primary dataset was sourced from an electronic medical record relational database, and key metrics such as sensitivity, specificity, and F1 score were used to evaluate model performance. Results: GPT-4o achieved superior performance with 100% sensitivity, specificity, and F1 score, outperforming the Clinical Longformer. Post-deployment, GPT-4o continued to perform flawlessly, identifying all positive PE cases without false positives or false negatives. The model successfully streamlined the clinical workflow, reducing the burden of manual review and enhancing diagnostic accuracy.