• 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

公众对医疗人工智能的看法:社交媒体的内容分析。

Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media.

机构信息

Faculty of Psychology, Beijing Normal University, Beijing, China.

School of Communication and Design, Sun Yat-Sen University, Guangzhou, China.

出版信息

J Med Internet Res. 2020 Jul 13;22(7):e16649. doi: 10.2196/16649.

DOI:10.2196/16649
PMID:32673231
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7385634/
Abstract

BACKGROUND

High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public's views on medical AI. Currently, the opinions of the general public on this matter remain unclear.

OBJECTIVE

The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors.

METHODS

Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis.

RESULTS

Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors.

CONCLUSIONS

Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients' emotional needs instead of focusing merely on technical issues.

摘要

背景

高质量的医疗资源在全球范围内都有很高的需求,人工智能(AI)在医疗保健中的应用可能有助于缓解与这种短缺相关的危机。医疗 AI 行业的发展在一定程度上取决于行业专家是否全面了解公众对医疗 AI 的看法。目前,公众对此事的意见尚不清楚。

目的

本研究旨在通过对社交媒体数据进行内容分析,探讨公众对医疗 AI 的看法,包括公众关注的具体话题;公众对医疗 AI 的态度及其原因;以及公众对 AI 是否可以替代人类医生的看法。

方法

通过应用编程接口,我们从新浪微博平台上爬取了 2017 年 1 月至 12 月期间来自中国各地的 1600 多万用户的数据集。基于这个数据集,我们通过内容分析确定了 2315 篇与医疗 AI 相关的帖子,并对其进行了分类。

结果

在确定的 2315 篇帖子中,我们发现平台上讨论了三种类型的 AI 话题:(1)技术与应用(n=987,42.63%)、(2)行业发展(n=706,30.50%)和(3)对社会的影响(n=622,26.87%)。在 956 篇表达公众态度的帖子中,有 59.4%(n=568)、34.4%(n=329)和 6.2%(n=59)的帖子分别表达了积极、中立和消极的态度。AI 技术的不成熟(27/59,46%)和对相关公司的不信任(n=15,25%)是持消极态度的两个主要原因。在 200 篇提到公众对用 AI 替代人类医生的态度的帖子中,有 47.5%(n=95)和 32.5%(n=65)的帖子分别表示 AI 将完全或部分替代人类医生。相比之下,20.0%(n=40)的帖子表示 AI 不会替代人类医生。

结论

我们的研究结果表明,人们最关心的是 AI 技术和应用。总的来说,大多数人持积极态度,认为 AI 医生将完全或部分替代人类医生。与以往对医学博士的研究相比,公众对医疗 AI 的态度更为积极。对 AI 的信任缺失和缺乏人文关怀因素是一些人仍然对医疗 AI 持消极态度的重要原因。我们建议从业者可能需要更加关注推广技术公司的可信度和满足患者的情感需求,而不仅仅是关注技术问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/7385634/84df43d9d8d0/jmir_v22i7e16649_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/7385634/84df43d9d8d0/jmir_v22i7e16649_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e8a/7385634/84df43d9d8d0/jmir_v22i7e16649_fig1.jpg

相似文献

1
Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media.公众对医疗人工智能的看法:社交媒体的内容分析。
J Med Internet Res. 2020 Jul 13;22(7):e16649. doi: 10.2196/16649.
2
Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data.新冠疫情期间中国社交媒体用户表达的担忧:对新浪微博数据的内容分析
J Med Internet Res. 2020 Nov 26;22(11):e22152. doi: 10.2196/22152.
3
Understanding public opinion regarding organ donation in China: A social media content analysis.理解中国公众对器官捐献的看法:社交媒体内容分析。
Sci Prog. 2021 Apr-Jun;104(2):368504211009665. doi: 10.1177/00368504211009665.
4
Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts.中文社交媒体中公众对痴呆症的话语和情绪:微博帖子的机器学习分析。
J Med Internet Res. 2022 Sep 2;24(9):e39805. doi: 10.2196/39805.
5
The Public Perception of the #GeneEditedBabies Event Across Multiple Social Media Platforms: Observational Study.多个社交媒体平台上公众对#基因编辑婴儿事件的认知:观察性研究
J Med Internet Res. 2022 Mar 11;24(3):e31687. doi: 10.2196/31687.
6
Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis.公众对新浪微博上焦虑障碍的态度:内容分析。
J Med Internet Res. 2023 Apr 4;25:e45777. doi: 10.2196/45777.
7
Educational and Emotional Needs of Patients with Myelodysplastic Syndromes: An AI Analysis of Multi-Country Social Media.骨髓增生异常综合征患者的教育和情感需求:多国社交媒体的人工智能分析。
Adv Ther. 2023 Jan;40(1):159-173. doi: 10.1007/s12325-022-02277-0. Epub 2022 Sep 22.
8
Attitudes of medical workers in China toward artificial intelligence in ophthalmology: a comparative survey.中国眼科医务人员对人工智能的态度:一项对比调查。
BMC Health Serv Res. 2021 Oct 9;21(1):1067. doi: 10.1186/s12913-021-07044-5.
9
Artificial Intelligence and Radiology: A Social Media Perspective.人工智能与放射学:社交媒体视角
Curr Probl Diagn Radiol. 2019 Jul-Aug;48(4):308-311. doi: 10.1067/j.cpradiol.2018.07.005. Epub 2018 Jul 23.
10
Humanitarian health computing using artificial intelligence and social media: A narrative literature review.利用人工智能和社交媒体进行人道主义健康计算:叙事文献综述。
Int J Med Inform. 2018 Jun;114:136-142. doi: 10.1016/j.ijmedinf.2018.01.015. Epub 2018 Jan 31.

引用本文的文献

1
Insights Into Perceived Worries Regarding the Adoption of Artificial Intelligence Among Intensive Care Unit Nurses in the West Bank.对约旦河西岸重症监护病房护士在采用人工智能方面所感知担忧的洞察。
SAGE Open Nurs. 2025 Sep 3;11:23779608251376177. doi: 10.1177/23779608251376177. eCollection 2025 Jan-Dec.
2
Physicians' attitudes and acceptance towards artificial intelligence in medical care: a qualitative study in Germany.医生对医疗保健中人工智能的态度与接受度:德国的一项定性研究
Front Digit Health. 2025 Jul 14;7:1616827. doi: 10.3389/fdgth.2025.1616827. eCollection 2025.
3
Perceptions, barriers, and risk of artificial intelligence Among healthcare professionals: A cross-sectional study.

本文引用的文献

1
A clinically applicable approach to continuous prediction of future acute kidney injury.一种临床适用的急性肾损伤未来发生的连续预测方法。
Nature. 2019 Aug;572(7767):116-119. doi: 10.1038/s41586-019-1390-1. Epub 2019 Jul 31.
2
Physician Confidence in Artificial Intelligence: An Online Mobile Survey.医生对人工智能的信心:一项在线移动调查。
J Med Internet Res. 2019 Mar 25;21(3):e12422. doi: 10.2196/12422.
3
Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners' Views.
医疗保健专业人员对人工智能的认知、障碍及风险:一项横断面研究。
Digit Health. 2025 Jul 16;11:20552076251360924. doi: 10.1177/20552076251360924. eCollection 2025 Jan-Dec.
4
Perceived worries in the adoption of artificial intelligence among nurses in neonatal intensive care units.新生儿重症监护病房护士在采用人工智能方面的感知担忧。
BMC Nurs. 2025 Jul 1;24(1):777. doi: 10.1186/s12912-025-03318-z.
5
Patients', clinicians' and developers' perspectives and experiences of artificial intelligence in cardiac healthcare: A qualitative study.患者、临床医生和开发者对人工智能在心脏保健领域的看法与体验:一项定性研究。
Digit Health. 2025 Jun 16;11:20552076251328578. doi: 10.1177/20552076251328578. eCollection 2025 Jan-Dec.
6
Barriers to the widespread adoption of diagnostic artificial intelligence for preventing antimicrobial resistance.阻碍诊断性人工智能广泛应用以预防抗菌药物耐药性的因素。
Sci Rep. 2025 Apr 16;15(1):13113. doi: 10.1038/s41598-025-95110-x.
7
Adapting Cognitive Behavioral Therapy for Adolescents in Iraq via Mobile Apps: Qualitative Study of Usability and Outcomes.通过移动应用程序为伊拉克青少年调整认知行为疗法:可用性和结果的定性研究
JMIR Pediatr Parent. 2025 Apr 11;8:e67137. doi: 10.2196/67137.
8
Unlocking the Power of AI: Healthcare Workforce Perception and Its Impact on their Work Performance in Saudi Arabia.释放人工智能的力量:沙特阿拉伯医疗保健劳动力的认知及其对工作绩效的影响。
Pak J Med Sci. 2025 Mar;41(3):682-686. doi: 10.12669/pjms.41.3.11014.
9
Artificial Intelligence in Medical Care - Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study.医疗保健中的人工智能——患者对护理关系和伦理的看法:一项定性研究
Health Expect. 2025 Apr;28(2):e70216. doi: 10.1111/hex.70216.
10
The need for an empirical research program regarding human-AI relational norms.开展关于人类与人工智能关系规范的实证研究项目的必要性。
AI Ethics. 2025;5(1):71-80. doi: 10.1007/s43681-024-00631-2. Epub 2025 Jan 9.
人工智能与初级医疗保健的未来:对英国全科医生观点的探索性定性研究
J Med Internet Res. 2019 Mar 20;21(3):e12802. doi: 10.2196/12802.
4
Characterizing Media Content and Effects of Organ Donation on a Social Media Platform: Content Analysis.在社交媒体平台上表征器官捐赠的媒体内容及影响:内容分析
J Med Internet Res. 2019 Mar 12;21(3):e13058. doi: 10.2196/13058.
5
Nature and Diffusion of Gynecologic Cancer-Related Misinformation on Social Media: Analysis of Tweets.社交媒体上妇科癌症相关错误信息的性质与传播:推文分析
J Med Internet Res. 2018 Oct 16;20(10):e11515. doi: 10.2196/11515.
6
Will artificial intelligence solve the human resource crisis in healthcare?人工智能能否解决医疗保健领域的人力资源危机?
BMC Health Serv Res. 2018 Jul 13;18(1):545. doi: 10.1186/s12913-018-3359-4.
7
Medical students' attitude towards artificial intelligence: a multicentre survey.医学生对人工智能的态度:一项多中心调查。
Eur Radiol. 2019 Apr;29(4):1640-1646. doi: 10.1007/s00330-018-5601-1. Epub 2018 Jul 6.
8
A content analysis of depression-related discourses on Sina Weibo: attribution, efficacy, and information sources.微博中有关抑郁的话语分析:归因、疗效和信息来源。
BMC Public Health. 2018 Jun 20;18(1):772. doi: 10.1186/s12889-018-5701-5.
9
Characterizing barriers to CPR training attainment using Twitter.利用 Twitter 描述心肺复苏培训达标面临的障碍。
Resuscitation. 2018 Jun;127:164-167. doi: 10.1016/j.resuscitation.2018.03.010. Epub 2018 Mar 12.
10
Artificial intelligence in healthcare: past, present and future.人工智能在医疗保健中的应用:过去、现在和未来。
Stroke Vasc Neurol. 2017 Jun 21;2(4):230-243. doi: 10.1136/svn-2017-000101. eCollection 2017 Dec.