• 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
Evaluation and Comparison of the Knowledge Levels of Current Artificial Intelligence Programs on Retinal/Vitreous Diseases and Treatment Methods.当前人工智能程序对视网膜/玻璃体疾病及治疗方法的知识水平评估与比较
J Curr Ophthalmol. 2024 Oct 16;36(1):78-81. doi: 10.4103/joco.joco_192_23. eCollection 2024 Jan-Mar.
2
Investigating the comparative superiority of artificial intelligence programs in assessing knowledge levels regarding ocular inflammation, uvea diseases, and treatment modalities.研究人工智能程序在评估眼部炎症、葡萄膜疾病及治疗方式相关知识水平方面的比较优势。
Taiwan J Ophthalmol. 2024 Sep 13;14(3):409-413. doi: 10.4103/tjo.TJO-D-23-00166. eCollection 2024 Jul-Sep.
3
The performance of ChatGPT-4 and Bing Chat in frequently asked questions about glaucoma.ChatGPT-4和必应聊天在青光眼常见问题方面的表现。
Eur J Ophthalmol. 2025 Jul;35(4):1323-1328. doi: 10.1177/11206721251321197. Epub 2025 Feb 19.
4
Assessing chatbots ability to produce leaflets on cataract surgery: Bing AI, chatGPT 3.5, chatGPT 4o, ChatSonic, Google Bard, Perplexity, and Pi.评估聊天机器人生成白内障手术宣传册的能力：必应人工智能、ChatGPT 3.5、ChatGPT 4、ChatSonic、谷歌巴德、Perplexity和Pi。
J Cataract Refract Surg. 2025 May 1;51(5):371-375. doi: 10.1097/j.jcrs.0000000000001622.
5
Examining the competence of artificial intelligence programs in neuro-ophthalmological disorders and analyzing their comparative superiority.检验人工智能程序在神经眼科疾病方面的能力并分析它们的相对优势。
Oman J Ophthalmol. 2024 Oct 24;17(3):348-351. doi: 10.4103/ojo.ojo_19_24. eCollection 2024 Sep-Dec.
6
Evaluation of Current Artificial Intelligence Programs on the Knowledge of Glaucoma.当前人工智能程序对青光眼知识的评估
Klin Monbl Augenheilkd. 2024 Oct;241(10):1140-1144. doi: 10.1055/a-2327-8484. Epub 2024 Jul 24.
7
How appropriately can generative artificial intelligence platforms, including GPT-4, Gemini, Bing, and Wrtn, answer questions about colon cancer in the Korean language?包括GPT-4、Gemini、必应和Wrtn在内的生成式人工智能平台，在回答韩语结肠癌相关问题时的恰当程度如何？
Ann Coloproctol. 2025 Jun;41(3):190-197. doi: 10.3393/ac.2024.00122.0017. Epub 2025 Jun 25.
8
Exploring Artificial Intelligence Programs' Understanding of Lens, Cataract, and Refractive Surgery Information.探索人工智能程序对晶状体、白内障和屈光手术信息的理解。
Middle East Afr J Ophthalmol. 2024 Sep 13;30(3):173-176. doi: 10.4103/meajo.meajo_199_23. eCollection 2023 Jul-Sep.
9
Is ChatGPT a more academic source than google searches for patient questions about hip arthroscopy? An analysis of the most frequently asked questions.对于患者关于髋关节镜检查的问题，ChatGPT 比谷歌搜索是更具学术性的信息来源吗？对最常见问题的分析。
J ISAKOS. 2025 Jun;12:100892. doi: 10.1016/j.jisako.2025.100892. Epub 2025 May 3.
10
A comparative study on the knowledge levels of artificial intelligence programs in diagnosing ophthalmic pathologies and intraocular tumors evaluated their superiority and potential utility.一项关于人工智能程序在诊断眼科疾病和眼内肿瘤方面知识水平的比较研究评估了它们的优越性和潜在效用。
Int Ophthalmol. 2023 Dec;43(12):4905-4909. doi: 10.1007/s10792-023-02893-x. Epub 2023 Oct 26.

引用本文的文献

1
Comment on "Evaluation and Comparison of the Knowledge Levels of Current Artificial Intelligence Programs on Retinal/Vitreous Diseases and Treatment Methods".关于《当前人工智能程序对视网膜/玻璃体疾病及治疗方法的知识水平评估与比较》的评论
J Curr Ophthalmol. 2025 Jun 5;36(3):312-313. doi: 10.4103/joco.joco_261_24. eCollection 2024 Jul-Sep.
2
Reply to Comment on "Evaluation and Comparison of the Knowledge Levels of Current Artificial Intelligence Programs on Retinal/Vitreous Diseases and Treatment Methods".对《当前人工智能程序在视网膜/玻璃体疾病及治疗方法方面的知识水平评估与比较》评论的回复
J Curr Ophthalmol. 2025 Jun 5;36(3):314. doi: 10.4103/joco.joco_268_24. eCollection 2024 Jul-Sep.
3
Current Artificial Intelligence Programs on Retinal/Vitreous Diseases and Treatment Methods.
J Curr Ophthalmol. 2025 Jun 5;36(3):310. doi: 10.4103/joco.joco_250_24. eCollection 2024 Jul-Sep.
4
Reply to Comment on "Evaluation and Comparison of the Knowledge Levels of Current Artificial Intelligence Programs on Retinal/Vitreous Diseases and Treatment Methods".对《当前人工智能程序在视网膜/玻璃体疾病及治疗方法方面的知识水平评估与比较》评论的回复
J Curr Ophthalmol. 2025 Jun 5;36(3):311. doi: 10.4103/joco.joco_266_24. eCollection 2024 Jul-Sep.

本文引用的文献

1
ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports.ChatGPT 让医学文献通俗易懂：简化放射学报告的探索性案例研究。
Eur Radiol. 2024 May;34(5):2817-2825. doi: 10.1007/s00330-023-10213-1. Epub 2023 Oct 5.
2
ChatGPT - Reshaping medical education and clinical management.ChatGPT——重塑医学教育与临床管理。
Pak J Med Sci. 2023 Mar-Apr;39(2):605-607. doi: 10.12669/pjms.39.2.7653.
3
The future of ChatGPT in academic research and publishing: A commentary for clinical and translational medicine.ChatGPT在学术研究与出版领域的未来：一篇针对临床与转化医学的评论
Clin Transl Med. 2023 Mar;13(3):e1207. doi: 10.1002/ctm2.1207.
4
Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models.ChatGPT在美国医师执照考试中的表现：使用大语言模型进行人工智能辅助医学教育的潜力。
PLOS Digit Health. 2023 Feb 9;2(2):e0000198. doi: 10.1371/journal.pdig.0000198. eCollection 2023 Feb.
5
How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment.ChatGPT在美国医师执照考试（USMLE）中的表现如何？大语言模型对医学教育和知识评估的影响。
JMIR Med Educ. 2023 Feb 8;9:e45312. doi: 10.2196/45312.
6
The current state of artificial intelligence in ophthalmology.人工智能在眼科学中的应用现状。
Surv Ophthalmol. 2019 Mar-Apr;64(2):233-240. doi: 10.1016/j.survophthal.2018.09.002. Epub 2018 Sep 22.
7
Deep learning applications in ophthalmology.深度学习在眼科中的应用。
Curr Opin Ophthalmol. 2018 May;29(3):254-260. doi: 10.1097/ICU.0000000000000470.
8
Electronic Health Records: Then, Now, and in the Future.电子健康记录：过去、现在与未来。
Yearb Med Inform. 2016 May 20;Suppl 1(Suppl 1):S48-61. doi: 10.15265/IYS-2016-s006.
9
The coming of age of artificial intelligence in medicine.人工智能在医学领域的成熟发展。
Artif Intell Med. 2009 May;46(1):5-17. doi: 10.1016/j.artmed.2008.07.017. Epub 2008 Sep 13.

当前人工智能程序对视网膜/玻璃体疾病及治疗方法的知识水平评估与比较

Evaluation and Comparison of the Knowledge Levels of Current Artificial Intelligence Programs on Retinal/Vitreous Diseases and Treatment Methods.

作者信息

Sensoy Eyupcan, Citirik Mehmet

机构信息

Department of Ophthalmology, Ankara Etlik City Hospital, Ankara, Turkey.

出版信息

J Curr Ophthalmol. 2024 Oct 16;36(1):78-81. doi: 10.4103/joco.joco_192_23. eCollection 2024 Jan-Mar.

DOI:10.4103/joco.joco_192_23
PMID:39553327
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11567598/
Abstract

PURPOSE

To evaluate the answers to multiple-choice questions about retina and vitreous diseases and treatment modalities of Chat Generative Pre-Trained Transformer (ChatGPT), Bard, and Bing artificial intelligence chatbots, examining the level of knowledge about these subjects, and investigating the existence of their superiority over each other.

METHODS

Forty-six questions related to retinal and vitreous diseases and treatment modalities were asked to ChatGPT, Bing, and Bard chatbots.

RESULTS

The Bing artificial intelligence chatbot correctly answered 76.1% of the questions. ChatGPT and Bard artificial intelligence chatbots correctly answered 60.9% of the questions. No statistically significant difference was observed between the rates of correct and incorrect answers to the questions on the three artificial intelligence chatbots ( = 0.206).

CONCLUSIONS

Artificial intelligence chatbots can be used to access accurate information about retinal and vitreous diseases and treatment modalities. However, the information obtained may not always be correct, and care should be taken about its use and results.

摘要

目的

评估聊天生成预训练变换器（ChatGPT）、巴德（Bard）和必应（Bing）人工智能聊天机器人对视网膜和玻璃体疾病及治疗方式的多项选择题的回答，考察它们对这些主题的知识水平，并探究它们相互之间是否存在优势。

方法

向ChatGPT、Bing和Bard聊天机器人提出了46个与视网膜和玻璃体疾病及治疗方式相关的问题。

结果

必应人工智能聊天机器人正确回答了76.1%的问题。ChatGPT和巴德人工智能聊天机器人正确回答了60.9%的问题。在这三个人工智能聊天机器人对问题的正确和错误回答率之间未观察到统计学上的显著差异（P = 0.206）。

结论

人工智能聊天机器人可用于获取有关视网膜和玻璃体疾病及治疗方式的准确信息。然而，获得的信息可能并不总是正确的，在使用其信息和结果时应谨慎。