Purpose To evaluate the performance of LLMs in extracting data from stroke CT reports in the presence and absence of an annotation guideline. Methods In this study, performance of GPT-4o and Llama-3.3-70B in extracting ten imaging findings from stroke CT reports was assessed in two datasets from a single academic stroke center. Dataset A (n = 200) was a stratified cohort including various pathological findings, whereas Dataset B (n = 100) was a consecutive cohort. Initially, an annotation guideline providing clear data extraction instructions was designed based on a review of cases with inter-annotator disagreements in dataset A. For each LLM, data extraction was performed under two conditions — with the annotation guideline included in the prompt and without it. Queries for both LLMs were run with a temperature setting of 0. For GPT-4o, additional queries with a temperature of 1 were performed. Results GPT-4o consistently demonstrated superior performance over Llama-3.3-70B under identical conditions, with micro-averaged precision ranging from 0.83 to 0.95 for GPT-4o and from 0.65 to 0.86 for Llama-3.3-70B. Across both models and both datasets, incorporating the annotation guideline into the LLM input resulted in higher precision rates, while recall rates largely remained stable. In dataset B, precision of GPT-4o and Llama-3-70B improved from 0.83 to 0.95 and from 0.87 to 0.94, respectively. The greatest increase in precision on a variable-level was seen in infarct demarcation (0.59 to 1.00) and subdural hematoma (0.67 to 1.00). Overall classification performance with and without annotation guideline was significantly different in five out of six conditions (e.g. dataset B/Llama-3.3/temp=0: p = 0.001). Conclusion Our results demonstrate the potential of GPT-4o and Llama-3.3-70B in extracting imaging findings from stroke CT reports, with GPT-4o steadily exceeding the performance of Llama-3-70B. We further provide evidence that well-defined annotation guidelines can enhance LLM data extraction accuracy.

FP is a full-time employee of Smart Reporting GmbH. PW is a consultant for Smart Reporting GmbH.

This study did not receive any funding

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Ethics committee/IRB of the Technical University of Munich gave ethical approval for this work

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The code for running the LLM queries is provided online at https://github.com/shk03/stroke_llm_data_extraction