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Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review
For instance, Huang et al [19] demonstrated that providing LLMs with example outputs for few-shot learning and chain-of-thought reasoning methods for prompting yielded higher classification performance compared to baseline zero-shot applications of LLMs for data extraction. The careful design of prompting methodologies personalized to specific tasks and clinical domains within oncology may yield more accurate and efficient data extraction performance [49].
JMIR Cancer 2025;11:e65984
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