Large Language Models vs. Machine Learning on Structured Perioperative Data: Does Model Choice Matter?
The study by Ko and colleagues provides evidence that large language models may achieve modestly improved performance compared with traditional machine learning models when applied to structured perioperative data. However, whether such incremental model performance gains can be translated into impr...
| Publicado en: | Journal of Medical Systems Vol. 50; no. 1; pp. 1 - 4 |
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| Autores principales: | , |
| Formato: | commentary Journal Article |
| Publicado: |
Springer Nature
6/9/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194419674&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194419674 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 6/9/2026 vid: 50 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194419674 194419674 194419674 10.1007/s10916-026-02422-0 194419674 ppf: 1 ppct: 3 formats: tig: atl: Large Language Models vs. Machine Learning on Structured Perioperative Data: Does Model Choice Matter? aug: au: Wingert, Theodora Dong, Xuezhi affil: https://ror.org/046rm7j60 Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, 757 Westwood Plaza, Suite 3325, Los Angeles, CA, USA sug: subj: Health Status Evaluation Patient Classification Electronic Health Records Natural Language Processing Prediction Models Machine Learning Algorithms Preoperative Period United States Medical Informatics Quality Improvement Classification Algorithms Decision Support Systems, Clinical Anesthesiologists Organizations Artificial Intelligence Automation Decision Making, Clinical ab: The study by Ko and colleagues provides evidence that large language models may achieve modestly improved performance compared with traditional machine learning models when applied to structured perioperative data. However, whether such incremental model performance gains can be translated into improvements in clinical decision-making, workflow integration, or patient outcomes remains an important question for future investigation. pubtype: Academic Journal doctype: commentary Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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