<ul><li>Extracting medical history entities (MHEs) from clinical text helps structure free-text clinical notes into standardized EHRs.</li><li>This study evaluates the performance of clinical large language models (cLLMs) in recognizing patient history entities.</li><li>The cLLMs showed potential in reducing the time required for extracting MHEs.</li><li>Fine-tuned GatorTron and GatorTronS demonstrated the highest performance in recognizing MHEs.</li></ul>

Extracting Patient History from Clinical Text: A Comparative Study of Clinical Large Language Models

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