• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
How do GPs Want Large Language Models to be Applied in Primary Care, and What Are Their Concerns? A Cross-Sectional Survey.全科医生希望大语言模型如何应用于基层医疗,他们的担忧是什么?一项横断面调查。
J Eval Clin Pract. 2025 Jun;31(4):e70129. doi: 10.1111/jep.70129.
2
Computerization and the future of primary care: A survey of general practitioners in the UK.计算机化与基层医疗的未来:对英国全科医生的调查。
PLoS One. 2018 Dec 12;13(12):e0207418. doi: 10.1371/journal.pone.0207418. eCollection 2018.
3
Perspectives and Experiences With Large Language Models in Health Care: Survey Study.医疗保健领域中大型语言模型的观点与经验:调查研究
J Med Internet Res. 2025 May 1;27:e67383. doi: 10.2196/67383.
4
GP or ChatGPT? Ability of large language models (LLMs) to support general practitioners when prescribing antibiotics.全科医生还是ChatGPT?大型语言模型在开具抗生素处方时支持全科医生的能力。
J Antimicrob Chemother. 2025 May 2;80(5):1324-1330. doi: 10.1093/jac/dkaf077.
5
Patient Online Record Access in English Primary Care: Qualitative Survey Study of General Practitioners' Views.患者在线记录在英国初级保健中的访问:全科医生观点的定性调查研究。
J Med Internet Res. 2023 Feb 22;25:e43496. doi: 10.2196/43496.
6
ChatGPT Use Among Pediatric Health Care Providers: Cross-Sectional Survey Study.儿科保健提供者中使用 ChatGPT:横断面调查研究。
JMIR Form Res. 2024 Sep 12;8:e56797. doi: 10.2196/56797.
7
Implementation of evidence-based knowledge in general practice.循证医学知识在全科医疗中的应用。
Dan Med J. 2017 Dec;64(12).
8
Leveraging Large Language Models for Precision Monitoring of Chemotherapy-Induced Toxicities: A Pilot Study with Expert Comparisons and Future Directions.利用大语言模型进行化疗诱导毒性的精准监测:一项专家比较及未来方向的试点研究
Cancers (Basel). 2024 Aug 12;16(16):2830. doi: 10.3390/cancers16162830.
9
Laypeople's Use of and Attitudes Toward Large Language Models and Search Engines for Health Queries: Survey Study.非专业人士使用大语言模型和搜索引擎进行健康查询的情况及态度:调查研究
J Med Internet Res. 2025 Feb 13;27:e64290. doi: 10.2196/64290.
10
Breastfeeding related knowledge, attitudes, perceptions and practices of primary healthcare professionals in Ireland: A national cross-sectional survey.爱尔兰基层医疗保健专业人员与母乳喂养相关的知识、态度、认知和实践:一项全国性横断面调查。
PLoS One. 2025 Apr 9;20(4):e0320763. doi: 10.1371/journal.pone.0320763. eCollection 2025.

本文引用的文献

1
Implications of Large Language Models for Clinical Practice: Ethical Analysis Through the Principlism Framework.大语言模型对临床实践的影响:通过原则主义框架进行伦理分析
J Eval Clin Pract. 2025 Feb;31(1):e14250. doi: 10.1111/jep.14250.
2
Using fine-tuned large language models to parse clinical notes in musculoskeletal pain disorders.使用微调的大语言模型解析肌肉骨骼疼痛障碍的临床记录。
Lancet Digit Health. 2023 Oct 26. doi: 10.1016/S2589-7500(23)00202-9.
3
Generative artificial intelligence in primary care: an online survey of UK general practitioners.初级保健中的生成式人工智能:英国全科医生的在线调查。
BMJ Health Care Inform. 2024 Sep 17;31(1):e101102. doi: 10.1136/bmjhci-2024-101102.
4
The potential for large language models to transform cardiovascular medicine.大语言模型改变心血管医学的潜力。
Lancet Digit Health. 2024 Oct;6(10):e767-e771. doi: 10.1016/S2589-7500(24)00151-1. Epub 2024 Aug 29.
5
Ethical and regulatory challenges of large language models in medicine.医学领域大型语言模型的伦理和监管挑战。
Lancet Digit Health. 2024 Jun;6(6):e428-e432. doi: 10.1016/S2589-7500(24)00061-X. Epub 2024 Apr 23.
6
Performance of GPT-4 in Membership of the Royal College of Paediatrics and Child Health-style examination questions.GPT-4 在英国皇家儿科学会式考试问题中的表现。
BMJ Paediatr Open. 2024 Mar 20;8(1):e002575. doi: 10.1136/bmjpo-2024-002575.
7
Performance of generative pre-trained Transformer-4 (GPT-4) in RCOG diploma-style questions.生成式预训练变换器-4(GPT-4)在皇家妇产科医师学院文凭式问题中的表现。
Postgrad Med J. 2024 Aug 16;100(1187):695-696. doi: 10.1093/postmj/qgae038.
8
The performance of large language models in intercollegiate Membership of the Royal College of Surgeons examination.大型语言模型在皇家外科学院会员联合考试中的表现。
Ann R Coll Surg Engl. 2024 Nov;106(8):700-704. doi: 10.1308/rcsann.2024.0023. Epub 2024 Mar 6.
9
Potential applications and implications of large language models in primary care.大语言模型在初级保健中的潜在应用和影响。
Fam Med Community Health. 2024 Jan 30;12(Suppl 1):e002602. doi: 10.1136/fmch-2023-002602.
10
Performance of Generative Pre-trained Transformer-4 (GPT-4) in Membership of the Royal College of General Practitioners (MRCGP)-style examination questions.生成式预训练变换器4(GPT-4)在皇家全科医师学院(MRCGP)风格考试问题中的表现。
Postgrad Med J. 2024 Mar 18;100(1182):274-275. doi: 10.1093/postmj/qgad128.

全科医生希望大语言模型如何应用于基层医疗,他们的担忧是什么?一项横断面调查。

How do GPs Want Large Language Models to be Applied in Primary Care, and What Are Their Concerns? A Cross-Sectional Survey.

作者信息

Armitage Richard C

机构信息

Academic Unit of Population and Lifespan Sciences, School of Medicine, University of Nottingham, Nottingham, UK.

出版信息

J Eval Clin Pract. 2025 Jun;31(4):e70129. doi: 10.1111/jep.70129.

DOI:10.1111/jep.70129
PMID:40369934
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12079004/
Abstract

INTRODUCTION

Although the potential utility of large language models (LLMs) in medicine and healthcare is substantial, no assessment has been made to date of how GPs want LLMs to be applied in primary care, or of which issues GPs are most concerned about regarding the implementation of LLMs into their clinical practice. This study's objective was to generate preliminary evidence that answers these questions, which are relevant because GPs themselves will ultimately harness the power of LLMs in primary care.

METHODS

Non-probability sampling was utilised: GPs practicing in the UK and who were members of one of two Facebook groups (one containing a community of UK primary care staff, the other containing a community of GMC-registered doctors in the UK) were invited to complete an online survey, which ran from 06 to 13 November 2024.

RESULTS

The survey received 113 responses, 107 of which were from GPs practicing in the UK. When LLM accuracy and safety were assumed to be guaranteed, broad enthusiasm for LLMs carrying out various nonclinical and clinical tasks in primary care was reported. The single nonclinical task and clinical task that respondents were most supportive of were the LLM listening to the consultation and writing notes in real-time for the GP to review, edit, and save (44.0%), and the LLM identifying outstanding clinical tasks and actioning them (51.0%), respectively. Respondents were concerned with a range of issues regarding LLMs being embedded into clinical systems, with patient safety being the most commonly reported single issue of concern (36.2%).

DISCUSSION

This study has generated preliminary evidence that is of potential utility to those developing LLMs for use in primary care. Further research is required to expand this evidence base to further inform the development of these technologies, and to ensure they are acceptable to the GPs who will use them.

摘要

引言

尽管大语言模型(LLMs)在医学和医疗保健领域具有巨大的潜在效用,但迄今为止,尚未对全科医生(GPs)希望大语言模型在初级保健中如何应用,或全科医生在将大语言模型应用于临床实践时最关心哪些问题进行评估。本研究的目的是生成初步证据来回答这些问题,这些问题具有相关性,因为全科医生自身最终将在初级保健中利用大语言模型的力量。

方法

采用非概率抽样:邀请在英国执业且是两个脸书群组之一的成员的全科医生(其中一个群组包含英国初级保健人员社区,另一个群组包含英国医学委员会注册医生社区)完成一项在线调查,该调查于2024年11月6日至13日进行。

结果

该调查共收到113份回复,其中107份来自在英国执业的全科医生。当假定大语言模型的准确性和安全性得到保证时,报告显示全科医生对大语言模型在初级保健中执行各种非临床和临床任务表现出广泛的热情。受访者最支持的单一非临床任务和临床任务分别是大语言模型实时听取会诊并为全科医生撰写笔记以供审查、编辑和保存(44.0%),以及大语言模型识别未完成的临床任务并采取行动(51.0%)。受访者对将大语言模型嵌入临床系统存在一系列担忧,患者安全是最常报告的单一担忧问题(36.2%)。

讨论

本研究生成了初步证据,对那些开发用于初级保健的大语言模型的人具有潜在效用。需要进一步研究以扩大这一证据基础,为这些技术的进一步发展提供更多信息,并确保它们为将使用它们的全科医生所接受。