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

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...

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Autores principales: Gao, Shuqing, He, Lingnan, Chen, Yue, Li, Dan, Lai, Kaisheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385634/
https://www.ncbi.nlm.nih.gov/pubmed/32673231
http://dx.doi.org/10.2196/16649
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author Gao, Shuqing
He, Lingnan
Chen, Yue
Li, Dan
Lai, Kaisheng
author_facet Gao, Shuqing
He, Lingnan
Chen, Yue
Li, Dan
Lai, Kaisheng
author_sort Gao, Shuqing
collection PubMed
description 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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spelling pubmed-73856342020-08-12 Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media Gao, Shuqing He, Lingnan Chen, Yue Li, Dan Lai, Kaisheng J Med Internet Res Original Paper 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. JMIR Publications 2020-07-13 /pmc/articles/PMC7385634/ /pubmed/32673231 http://dx.doi.org/10.2196/16649 Text en ©Shuqing Gao, Lingnan He, Yue Chen, Dan Li, Kaisheng Lai. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.07.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Gao, Shuqing
He, Lingnan
Chen, Yue
Li, Dan
Lai, Kaisheng
Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
title Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
title_full Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
title_fullStr Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
title_full_unstemmed Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
title_short Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
title_sort public perception of artificial intelligence in medical care: content analysis of social media
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385634/
https://www.ncbi.nlm.nih.gov/pubmed/32673231
http://dx.doi.org/10.2196/16649
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