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

被引:78
|
作者
Gao, Shuqing [1 ]
He, Lingnan [2 ,3 ]
Chen, Yue [2 ]
Li, Dan [4 ]
Lai, Kaisheng [4 ]
机构
[1] Beijing Normal Univ, Fac Psychol, Beijing, Peoples R China
[2] Sun Yat Sen Univ, Sch Commun & Design, Guangzhou, Peoples R China
[3] Guangdong Key Lab Big Data Anal & Simulat Publ Op, Guangzhou, Peoples R China
[4] Jinan Univ, Sch Journalism & Commun, 601 Whampoa Ave W, Guangzhou, Peoples R China
关键词
artificial intelligence; public perception; social media; content analysis; medical care; INFORMATION; INTERNET; TWITTER; FUTURE; CANCER;
D O I
10.2196/16649
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence-Enabled Social Media Analysis
    Cresswell, Kathrin
    Tahir, Ahsen
    Sheikh, Zakariya
    Hussain, Zain
    Hernandez, Andres Dominguez
    Harrison, Ewen
    Williams, Robin
    Sheikh, Aziz
    Hussain, Amir
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2021, 23 (05)
  • [42] Arming the public with artificial intelligence to counter social bots
    Yang, Kai-Cheng
    Varol, Onur
    Davis, Clayton A.
    Ferrara, Emilio
    Flammini, Alessandro
    Menczer, Filippo
    [J]. HUMAN BEHAVIOR AND EMERGING TECHNOLOGIES, 2019, 1 (01) : 48 - 61
  • [43] Generative artificial intelligence and its impact on media content creation
    Franganillo, Jorge
    [J]. METHAODOS-REVISTA DE CIENCIAS SOCIALES, 2023, 11 (02):
  • [44] Current perception of social accountability of medical schools in Japan: A qualitative content analysis
    Mori, Hiroko
    Izumiya, Masashi
    Hayashi, Mikio
    Eto, Masato
    [J]. MEDICAL TEACHER, 2023, 45 (05) : 524 - 531
  • [45] Social Asymmetry, Artificial Intelligence and the Medical Imaging Landscape
    Currie, Geoffrey
    Rohren, Eric
    [J]. SEMINARS IN NUCLEAR MEDICINE, 2022, 52 (04) : 498 - 503
  • [46] Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence
    Hajli, Nick
    Saeed, Usman
    Tajvidi, Mina
    Shirazi, Farid
    [J]. BRITISH JOURNAL OF MANAGEMENT, 2022, 33 (03) : 1238 - 1253
  • [47] Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes
    Owusu, Priscilla N.
    Reininghaus, Ulrich
    Koppe, Georgia
    Dankwa-Mullan, Irene
    Baernighausen, Till
    [J]. PLOS ONE, 2021, 16 (11):
  • [48] Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence
    Kieslich, Kimon
    Keller, Birte
    Starke, Christopher
    [J]. BIG DATA & SOCIETY, 2022, 9 (01):
  • [49] Prediction of Perception of Security Using Social Media Content
    Pulido, Cristian
    Fernanda Chaparro, Luisa
    Rudas, Jorge
    Victorino, Jorge
    Estrada, Camilo
    Angela Narvaez, Luz
    Gomez, Francisco
    [J]. PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2021, 2021, 12702 : 88 - 96
  • [50] Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum
    Ayers, John W.
    Poliak, Adam
    Dredze, Mark
    Leas, Eric C.
    Zhu, Zechariah
    Kelley, Jessica B.
    Faix, Dennis J.
    Goodman, Aaron M.
    Longhurst, Christopher A.
    Hogarth, Michael
    Smith, Davey M.
    [J]. JAMA INTERNAL MEDICINE, 2023, 183 (06) : 589 - 596