IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v338y2023ics0277953623007141.html
   My bibliography  Save this article

Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis

Author

Listed:
  • Vo, Vinh
  • Chen, Gang
  • Aquino, Yves Saint James
  • Carter, Stacy M.
  • Do, Quynh Nga
  • Woode, Maame Esi

Abstract

Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare from patients, the general public and health professionals’ perspectives to understand these issues from multiple perspectives.

Suggested Citation

  • Vo, Vinh & Chen, Gang & Aquino, Yves Saint James & Carter, Stacy M. & Do, Quynh Nga & Woode, Maame Esi, 2023. "Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis," Social Science & Medicine, Elsevier, vol. 338(C).
    • Handle: RePEc:eee:socmed:v:338:y:2023:i:c:s0277953623007141
    DOI: 10.1016/j.socscimed.2023.116357
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953623007141
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socscimed.2023.116357?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    2. Andreas Behr & Katja Theune, 2017. "Health System Efficiency: A Fragmented Picture Based on OECD Data," PharmacoEconomics - Open, Springer, vol. 1(3), pages 203-221, September.
    3. Wenjuan Fan & Jingnan Liu & Shuwan Zhu & Panos M. Pardalos, 2020. "Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)," Annals of Operations Research, Springer, vol. 294(1), pages 567-592, November.
    4. Charlotte Blease & Anna Kharko & Cosima Locher & Catherine M DesRoches & Kenneth D Mandl, 2020. "US primary care in 2029: A Delphi survey on the impact of machine learning," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-18, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Changdong, 2024. "How consumers respond to service failures caused by algorithmic mistakes: The role of algorithmic interpretability," Journal of Business Research, Elsevier, vol. 176(C).
    2. Tinglong Dai & Sridhar Tayur, 2022. "Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4443-4451, December.
    3. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    4. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    5. Tse, Tiffany Tsz Kwan & Hanaki, Nobuyuki & Mao, Bolin, 2024. "Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment," Journal of Economic Psychology, Elsevier, vol. 102(C).
    6. Gansser, Oliver Alexander & Reich, Christina Stefanie, 2021. "A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application," Technology in Society, Elsevier, vol. 65(C).
    7. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    8. Zhao, Taiyang & Ran, Yaxuan & Wu, Banggang & Lynette Wang, Valerie & Zhou, Liying & Lu Wang, Cheng, 2024. "Virtual versus human: Unraveling consumer reactions to service failures through influencer types," Journal of Business Research, Elsevier, vol. 178(C).
    9. Fan, Xiaoming, 2024. "Fintech platforms and information service quality from the perspective of investor cognition," Technology in Society, Elsevier, vol. 77(C).
    10. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.
    11. repec:cup:judgdm:v:15:y:2020:i:3:p:449-451 is not listed on IDEAS
    12. Felix Holzmeister & Martin Holmén & Michael Kirchler & Matthias Stefan & Erik Wengström, 2023. "Delegation Decisions in Finance," Management Science, INFORMS, vol. 69(8), pages 4828-4844, August.
    13. Poushneh, Atieh & Vasquez-Parraga, Arturo & Gearhart, Richard S., 2024. "The effect of empathetic response and consumers’ narcissism in voice-based artificial intelligence," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    14. Chiara Longoni & Andrea Bonezzi & Carey K. Morewedge, 2020. "Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Becksted (2020)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(3), pages 446-448, May.
    15. Yang, Yikai & Zheng, Jiehui & Yu, Yining & Qiu, Yiling & Wang, Lei, 2024. "The role of recommendation sources and attribute framing in online product recommendations," Journal of Business Research, Elsevier, vol. 174(C).
    16. Brüns, Jasper David & Meißner, Martin, 2024. "Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    17. Weiguang Wang & Guodong (Gordon) Gao & Ritu Agarwal, 2024. "Friend or Foe? Teaming Between Artificial Intelligence and Workers with Variation in Experience," Management Science, INFORMS, vol. 70(9), pages 5753-5775, September.
    18. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    19. David A. Schweidel & Yakov Bart & J. Jeffrey Inman & Andrew T. Stephen & Barak Libai & Michelle Andrews & Ana Babić Rosario & Inyoung Chae & Zoey Chen & Daniella Kupor & Chiara Longoni & Felipe Thomaz, 2022. "How consumer digital signals are reshaping the customer journey," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1257-1276, November.
    20. Zhang, Yaqiong & Wang, Shifu, 2023. "The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    21. Chen Yang & Jing Hu, 2022. "When do consumers prefer AI-enabled customer service? The interaction effect of brand personality and service provision type on brand attitudes and purchase intentions," Journal of Brand Management, Palgrave Macmillan, vol. 29(2), pages 167-189, March.

    More about this item

    Keywords

    Artificial intelligence; Healthcare; Health professional; General public; Patients;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:338:y:2023:i:c:s0277953623007141. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.