A Comparison of a Large Language Model vs Manual Chart Review for the Extraction of Data Elements From the Electronic Health Record
Large language models (LLMs) hold tremendous potential for accelerating clinical research and augmenting clinical care. One of the most promising LLM use cases is natural language processing (NLP) and the extraction of elements from unstructured text, for which LLMs may be superior to existing NLP software packages.1,2 Due to its standardized Liver Imaging Reporting and Data System (LI-RADS),3 hepatocellular carcinoma (HCC) imaging provides an ideal test case for LLM-enabled NLP data extraction from unstructured text.
