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Cited by 221Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AISentiment Analysis and Opinion Mining

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

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Gao, Shuqing, He, Lingnan, Chen, Yue, Li, Dan, Lai, Kaisheng·2020·Journal of Medical Internet Research
DOI 10.2196/16649PMC PMC7385634

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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.

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  • The public attitude and acceptance of digital health care systems remains a major barrier.

    “On a more general note, the public attitude and acceptance of digital health care systems remains a major barrier (53, 92–94).”

    From Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial (2024) · cites this paper as [92]

    The Introduction (Background) says public attitudes and acceptance can affect the development of medical AI, and the Discussion (Principal Results) identifies distrust and other concerns among some users. But the study concerns medical AI rather than digital health care systems generally, and its Discussion reports that nearly 60% of posts were positive, so it does not establish public acceptance as a major barrier overall. (LLM, conf=0.86)

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Authors
Gao, S., He, L., Chen, Y., Li, D., & Lai, K.
Affiliations
  • Beijing Normal University (CN)
  • Sun Yat-sen University (CN)
  • Jinan University (CN)
Corresponding author
Lai, Kaisheng
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2020-07-13
Volume
22
Issue
7
Pages
e16649
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/16649
PMC
PMC7385634
OpenAlex
W3042865117
Cited by
221
References
40
Citations by year
2026: 32 · 2025: 53 · 2024: 60 · 2023: 24 · 2022: 27 · 2021: 23 · 2020: 2
FWCI
5.63
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Social Science Fund of China (18ZDA165)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
public perception, artificial intelligence in healthcare, public attitudes toward AI, social media discourse, Weibo
MeSH terms
Adult, Artificial Intelligence, Humans, Perception, Public Opinion, Social Media

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