ArticleJMIR medical informatics2025
Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study.
Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Automated Esophageal Cancer Staging From Free-Text Radiology Reports: Large Language Model Evaluation Study.JMIR medical informatics · 2025Article
- Evaluating prompt and data perturbation sensitivity in large language models for radiology reports classification.JAMIA open · 2025Article
- Assessing the Accuracy of Diagnostic Capabilities of Large Language Models.Diagnostics (Basel, Switzerland) · 2025Article
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Abstract
backgroundPulmonary embolism (PE) is a critical condition requiring rapid diagnosis to reduce mortality. Extracting PE diagnoses from radiology reports manually is time-consuming, highlighting the need for automated solutions. Advances in natural language processing, especially transformer models like GPT-4o, offer promising tools to improve diagnostic accuracy and workflow efficiency in clinical settings.
objectiveThis study aimed to develop an automatic extraction system using GPT-4o to extract PE diagnoses from radiology report impressions, enhancing clinical decision-making and workflow efficiency.
methodsIn total, 2 approaches were developed and evaluated: a fine-tuned Clinical Longformer as a baseline model and a GPT-4o-based extractor. Clinical Longformer, an encoder-only model, was chosen for its robustness in text classification tasks, particularly on smaller scales. GPT-4o, a decoder-only instruction-following LLM, was selected for its advanced language understanding capabilities. The study aimed to evaluate GPT-4o's ability to perform text classification compared to the baseline Clinical Longformer. The Clinical Longformer was trained on a dataset of 1000 radiology report impressions and validated on a separate set of 200 samples, while the GPT-4o extractor was validated using the same 200-sample set. Postdeployment performance was further assessed on an additional 200 operational records to evaluate model efficacy in a real-world setting.
resultsGPT-4o outperformed the Clinical Longformer in 2 of the metrics, achieving a sensitivity of 1.0 (95% CI 1.0-1.0; Wilcoxon test, P<.001) and an F
conclusionsThe GPT-4o model provides an effective solution for the automatic extraction of PE diagnoses from radiology reports, offering a reliable tool that aids timely and accurate clinical decision-making. This approach has the potential to significantly improve patient outcomes by expediting diagnosis and treatment pathways for critical conditions like PE.
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