Suppr超能文献

基于日本循环学会2022年非心脏手术围手术期心血管评估与管理指南的术前评估大语言模型的演变

Evolution of a Large Language Model for Preoperative Assessment Based on the Japanese Circulation Society 2022 Guideline on Perioperative Cardiovascular Assessment and Management for Non-Cardiac Surgery.

作者信息

Kamihara Takahiro, Tabuchi Masanori, Omura Takuya, Suzuki Yumi, Aritake Tsukasa, Hirashiki Akihiro, Kokubo Manabu, Shimizu Atsuya

机构信息

Department of Cardiology, National Center for Geriatrics and Gerontology Obu Japan.

Department of Nursing, National Center for Geriatrics and Gerontology Obu Japan.

出版信息

Circ Rep. 2024 Mar 15;6(4):142-148. doi: 10.1253/circrep.CR-24-0019. eCollection 2024 Apr 10.

Abstract

The Japanese Circulation Society 2022 Guideline on Perioperative Cardiovascular Assessment and Management for Non-Cardiac Surgery standardizes preoperative cardiovascular assessments. The present study investigated the efficacy of a large language model (LLM) in providing accurate responses meeting the JCS 2022 Guideline. Data on consultation requests, physicians' cardiovascular records, and patients' response content were analyzed. Virtual scenarios were created using real-world clinical data, and a LLM was then consulted for such scenarios. Google BARD could accurately provide responses in accordance with the JCS 2022 Guideline in low-risk cases. Google Gemini has significantly improved its accuracy in intermediate- and high-risk cases.

摘要

日本循环学会2022年非心脏手术围手术期心血管评估与管理指南对术前心血管评估进行了标准化。本研究调查了大语言模型(LLM)在提供符合JCS 2022指南的准确回答方面的有效性。分析了咨询请求数据、医生的心血管记录和患者的回复内容。使用真实世界的临床数据创建虚拟场景,然后针对这些场景咨询大语言模型。谷歌BARD在低风险病例中能够准确地根据JCS 2022指南提供回答。谷歌Gemini在中高风险病例中的准确性有了显著提高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a28e/11004031/7b7026ed93f9/circrep-6-142-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验