Kamihara Takahiro, Tabuchi Masanori, Omura Takuya, Suzuki Yumi, Aritake Tsukasa, Hirashiki Akihiro, Kokubo Manabu, Shimizu Atsuya
Department of Cardiology, National Center for Geriatrics and Gerontology Obu Japan.
Department of Nursing, National Center for Geriatrics and Gerontology Obu Japan.
Circ Rep. 2024 Mar 15;6(4):142-148. doi: 10.1253/circrep.CR-24-0019. eCollection 2024 Apr 10.
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.
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。