• 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

Pearls and Pitfalls for LLMs 2.0.

作者信息

Huisman Merel, Kitamura Felipe, Cook Tessa S, Hentel Keith D, Elias Jonathan, Shih George, Moy Linda

机构信息

From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6500 HB Nijmegen, the Netherlands (M.H.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (F.K.); Bunkerhill Health, San Francisco, Calif (F.K.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Departments of Radiology (K.D.H., G.S.), Primary Care (J.E.), and Population Health Sciences (J.E.), Weill Cornell Medicine, New York, NY; and Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.).

出版信息

Radiology. 2024 Oct;313(1):e242512. doi: 10.1148/radiol.242512.

DOI:10.1148/radiol.242512
PMID:39470427
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11535876/
Abstract
摘要

相似文献

1
Pearls and Pitfalls for LLMs 2.0.大语言模型2.0的要点与陷阱
Radiology. 2024 Oct;313(1):e242512. doi: 10.1148/radiol.242512.
2
Large Language Models in Ophthalmology: Potential and Pitfalls.大语言模型在眼科学中的应用：潜力与陷阱。
Semin Ophthalmol. 2024 May;39(4):289-293. doi: 10.1080/08820538.2023.2300808. Epub 2024 Jan 5.
3
The first step is the hardest: pitfalls of representing and tokenizing temporal data for large language models.第一步是最困难的：为大型语言模型表示和标记时间数据的陷阱。
J Am Med Inform Assoc. 2024 Sep 1;31(9):2151-2158. doi: 10.1093/jamia/ocae090.
4
Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Health Care Professionals.大语言模型与用户信任：自我参照学习循环的后果及医疗保健专业人员的技能退化
J Med Internet Res. 2024 Apr 25;26:e56764. doi: 10.2196/56764.
5
Large Language Models in Medicine: The Potentials and Pitfalls : A Narrative Review.医学领域的大型语言模型：潜力与陷阱：一篇叙事性综述。
Ann Intern Med. 2024 Feb;177(2):210-220. doi: 10.7326/M23-2772. Epub 2024 Jan 30.
6
Assessing the research landscape and clinical utility of large language models: a scoping review.评估大型语言模型的研究现状和临床实用性：范围综述。
BMC Med Inform Decis Mak. 2024 Mar 12;24(1):72. doi: 10.1186/s12911-024-02459-6.
7
Pearls, pitfalls, and updates in toxicology.毒理学中的要点、陷阱及新进展
Emerg Med Clin North Am. 1997 May;15(2):427-49. doi: 10.1016/s0733-8627(05)70309-6.
8
From Bench to Bedside With Large Language Models: Expert Panel Narrative Review.从基础研究到临床应用的大型语言模型：专家小组叙事性综述。
AJR Am J Roentgenol. 2024 Sep;223(3):e2430928. doi: 10.2214/AJR.24.30928. Epub 2024 Apr 10.
9
ChatGPT, Bard, and Large Language Models for Biomedical Research: Opportunities and Pitfalls.ChatGPT、Bard 及大型语言模型在生物医学研究中的应用：机遇与挑战。
Ann Biomed Eng. 2023 Dec;51(12):2647-2651. doi: 10.1007/s10439-023-03284-0. Epub 2023 Jun 16.
10
Pearls and Pitfalls in Neck Imaging.颈部影像学的要点与陷阱
Neuroimaging Clin N Am. 2022 May;32(2):375-390. doi: 10.1016/j.nic.2022.02.002.

引用本文的文献

1
[Artificial intelligence in radiology : Literature overview and reading recommendations].[放射学中的人工智能：文献综述与阅读建议]
Radiologie (Heidelb). 2025 Apr;65(4):266-270. doi: 10.1007/s00117-025-01419-z. Epub 2025 Feb 4.

本文引用的文献

1
ChatGPT: Evaluating answers on contrast media related questions and finetuning by providing the model with the ESUR guideline on contrast agents.评估与对比剂相关问题的答案，并通过提供 ESUR 对比剂指南来对模型进行微调。
Curr Probl Diagn Radiol. 2024 Jul-Aug;53(4):488-493. doi: 10.1067/j.cpradiol.2024.04.005. Epub 2024 Apr 21.
2
Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy.GPT-4 在检测放射科报告错误方面的潜力：对报告准确性的影响。
Radiology. 2024 Apr;311(1):e232714. doi: 10.1148/radiol.232714.
3
Assessment of the efficacy of alkaline water in conjunction with conventional medication for the treatment of chronic gouty arthritis: A randomized controlled study.碱性水联合常规药物治疗慢性痛风性关节炎的疗效评估：一项随机对照研究。
Medicine (Baltimore). 2024 Apr 5;103(14):e37589. doi: 10.1097/MD.0000000000037589.
4
Short-Term Strategies for Augmenting the National Radiologist Workforce.短期增加国家放射科医生劳动力的策略。
AJR Am J Roentgenol. 2024 Jun;222(6):e2430920. doi: 10.2214/AJR.24.30920. Epub 2024 Apr 3.
5
Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI.用于评估由生成式人工智能驱动的医疗对话有效性的基础指标。
NPJ Digit Med. 2024 Mar 29;7(1):82. doi: 10.1038/s41746-024-01074-z.
6
Assessing the research landscape and clinical utility of large language models: a scoping review.评估大型语言模型的研究现状和临床实用性：范围综述。
BMC Med Inform Decis Mak. 2024 Mar 12;24(1):72. doi: 10.1186/s12911-024-02459-6.
7
Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.用户为什么会忽略警报？利用大语言模型总结评论并优化临床决策支持。
J Am Med Inform Assoc. 2024 May 20;31(6):1388-1396. doi: 10.1093/jamia/ocae041.
8
Adapted large language models can outperform medical experts in clinical text summarization.经过改编的大型语言模型在临床文本总结方面的表现优于医学专家。
Nat Med. 2024 Apr;30(4):1134-1142. doi: 10.1038/s41591-024-02855-5. Epub 2024 Feb 27.
9
Leveraging explainable artificial intelligence to optimize clinical decision support.利用可解释人工智能优化临床决策支持。
J Am Med Inform Assoc. 2024 Apr 3;31(4):968-974. doi: 10.1093/jamia/ocae019.
10
Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications.放射科中的聊天机器人和大型语言模型：临床和研究应用的实用入门指南。
Radiology. 2024 Jan;310(1):e232756. doi: 10.1148/radiol.232756.