• 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

Comment on "Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments".

作者信息

Tiffet Théophile, Beltramin Diva, Trombert-Paviot Béatrice, Bousquet Cédric

机构信息

Public Health and Medical Information Unit, University Hospital of Saint-Étienne, Avenue Albert Raimond, 42270, Saint-Priest-en-Jarez, France.

Laboratoire Inserm, SAINBIOSE, U1059, Dysfonction Vasculaire et Hémostase, University Jean-Monnet, 42000, Saint-Étienne, France.

出版信息

Drug Saf. 2025 Sep 2. doi: 10.1007/s40264-025-01592-z.

DOI:10.1007/s40264-025-01592-z
PMID:40892375
Abstract
摘要

相似文献

1
Comment on "Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments".关于《大型语言模型在命名实体识别中的性能与可重复性:在受控环境中使用的考量》的评论
Drug Saf. 2025 Sep 2. doi: 10.1007/s40264-025-01592-z.
2
Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments.大型语言模型在命名实体识别中的性能与可重复性:在受控环境中使用的考量
Drug Saf. 2025 Mar;48(3):287-303. doi: 10.1007/s40264-024-01499-1. Epub 2024 Dec 11.
3
Authors' response to Tiffet et al.'s comment on "Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments".作者对蒂菲特等人就《大型语言模型在命名实体识别中的性能与可重复性:在受控环境中使用的考量》所发表评论的回应。
Drug Saf. 2025 Sep 2. doi: 10.1007/s40264-025-01590-1.
4
Toward Cross-Hospital Deployment of Natural Language Processing Systems: Model Development and Validation of Fine-Tuned Large Language Models for Disease Name Recognition in Japanese.迈向自然语言处理系统的跨医院部署:用于日语疾病名称识别的微调大语言模型的模型开发与验证
JMIR Med Inform. 2025 Jul 8;13:e76773. doi: 10.2196/76773.
5
From BERT to generative AI - Comparing encoder-only vs. large language models in a cohort of lung cancer patients for named entity recognition in unstructured medical reports.从BERT到生成式人工智能——在一组肺癌患者中比较仅编码器模型与大语言模型用于非结构化医疗报告中的命名实体识别
Comput Biol Med. 2025 Sep;195:110665. doi: 10.1016/j.compbiomed.2025.110665. Epub 2025 Jun 24.
6
Clinical named entity recognition and relation extraction using natural language processing of medical free text: A systematic review.临床命名实体识别和关系抽取技术在医学自然语言处理中的应用:系统综述。
Int J Med Inform. 2023 Sep;177:105122. doi: 10.1016/j.ijmedinf.2023.105122. Epub 2023 Jun 5.
7
Zero- and few-shot Named Entity Recognition and Text Expansion in medication prescriptions using large language models.使用大语言模型在药物处方中进行零样本和少样本命名实体识别及文本扩展
Artif Intell Med. 2025 Sep;167:103165. doi: 10.1016/j.artmed.2025.103165. Epub 2025 Jun 20.
8
Artificial intelligence in healthcare text processing: a review applied to named entity recognition.医疗文本处理中的人工智能:应用于命名实体识别的综述
Front Artif Intell. 2025 Jul 7;8:1584203. doi: 10.3389/frai.2025.1584203. eCollection 2025.
9
The agreement of phonetic transcriptions between paediatric speech and language therapists transcribing a disordered speech sample.儿科言语和语言治疗师转写语音样本的音标转录的一致性。
Int J Lang Commun Disord. 2024 Sep-Oct;59(5):1981-1995. doi: 10.1111/1460-6984.13043. Epub 2024 Jun 8.
10
Prescription of Controlled Substances: Benefits and Risks管制药品的处方:益处与风险

本文引用的文献

1
Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine.变革医疗保健:大语言模型在医学领域的变革性影响。
J Med Internet Res. 2025 Jan 7;27:e59069. doi: 10.2196/59069.
2
Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments.大型语言模型在命名实体识别中的性能与可重复性:在受控环境中使用的考量
Drug Saf. 2025 Mar;48(3):287-303. doi: 10.1007/s40264-024-01499-1. Epub 2024 Dec 11.
3
Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology - a recent scoping review.
使用大型语言模型(如 ChatGPT)进行诊断医学的挑战和障碍,重点是数字病理学——近期的范围综述。
Diagn Pathol. 2024 Feb 27;19(1):43. doi: 10.1186/s13000-024-01464-7.
4
The imperative for regulatory oversight of large language models (or generative AI) in healthcare.对医疗保健领域的大语言模型(或生成式人工智能)进行监管监督的必要性。
NPJ Digit Med. 2023 Jul 6;6(1):120. doi: 10.1038/s41746-023-00873-0.
5
The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers.ChatGPT、生成式语言模型和人工智能在医学教育中的作用:与ChatGPT的对话及论文征集
JMIR Med Educ. 2023 Mar 6;9:e46885. doi: 10.2196/46885.