Retrospective analysis compares large language models and conventional machine learning in predicting postoperative outcomes, suggesting the need for further validation across various settings.
The findings support the versatility and efficiency of LLMs for clinical decision support through on-premises compatibility, addressing data privacy. Further validation with diverse datasets is needed to ensure their reliability and applicability across different perioperative settings.
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Lee et al. (2026) studied this question.
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