Comparing performance of seven fine-tuned open-source large language models in summarizing and predicting outcome-relevant information from mechanical thrombectomy reports in patients with acute ischemic stroke

Abstract

Question Specifically fine-tuned Large Language Models (LLMs) can improve radiology workflow by automatically summarizing thrombectomy reports and inferring angiographic classifications from textual descriptions. Findings Fine-tuned LLMs achieve similar performance in summarizing thrombectomy reports, with each model performing best in specific categories and showing moderate accuracy in correct "Thrombolysis-In-Cerebral-Ischemia (TICI)" Score prediction (66-71%). Clinical relevance Integrating fine-tuned LLMs into radiology workflows may accelerate decision-making and improve patient outcomes by automatically summarizing reports and assessing recanalization success, while future work should enhance contextual understanding, address ambiguous inputs, and limit hallucinations.

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