IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-96-9697-0_33.html
   My bibliography  Save this book chapter

A Study on Willingness to Use Healthcare AI Based on TAM Modelling

Author

Listed:
  • Huiying Du

    (Beijing Information Science & Technology University)

  • Weijie Han

    (Beijing Information Science & Technology University)

  • Chen Yao

    (Beijing Information Science & Technology University)

  • Genxiang Gao

    (Beijing Information Science & Technology University)

Abstract

With the rapid development of the internet and artificial intelligence technologies, medical AI has demonstrated significant potential in areas such as image diagnosis and robotic assistance, providing greater assurance for patient health. Although the widespread adoption of medical AI is inevitable, it has not yet been extensively implemented. To address this, we have constructed a model for consumer intention to use medical AI based on the Technology Acceptance Model (TAM), incorporating subjective norms, trust, and external factors. We collected 684 valid questionnaires through online and offline methods and analyzed the data using SmartPLS 3.0 software and SPSS data processing software for reliability and validity analysis. We also tested the overall fit of each indicator variable and determined whether the hypothetical model was valid through path coefficients and p-values. The empirical results allow us to infer the factors influencing the promotion of medical AI and to propose suggestions for the development and improvement of medical AI.

Suggested Citation

  • Huiying Du & Weijie Han & Chen Yao & Genxiang Gao, 2025. "A Study on Willingness to Use Healthcare AI Based on TAM Modelling," Lecture Notes in Operations Research,, Springer.
  • Handle: RePEc:spr:lnopch:978-981-96-9697-0_33
    DOI: 10.1007/978-981-96-9697-0_33
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:spr:lnopch:978-981-96-9697-0_33. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.