IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04746007.html
   My bibliography  Save this paper

Does improving diagnostic accuracy increase artificial intelligence adoption? A public acceptance survey using randomized scenarios of diagnostic methods

Author

Listed:
  • Yulin Hswen

    (UC San Francisco - University of California [San Francisco] - UC - University of California, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Ismaël Rafaï

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Antoine Lacombe

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Bérengère Davin-Casalena

    (ORS PACA - Observatoire régional de la santé Provence-Alpes-Côte d'Azur [Marseille])

  • Dimitri Dubois

    (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier, UM - Université de Montpellier, CNRS - Centre National de la Recherche Scientifique)

  • Thierry Blayac

    (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier)

  • Bruno Ventelou

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

This study examines the acceptance of artificial intelligence (AI)-based diagnostic alternatives compared to traditional biological testing through a randomized scenario experiment in the domain of neurodegenerative diseases (NDs). A total of 3225 pairwise choices of ND risk-prediction tools were offered to participants, with 1482 choices comparing AI with the biological saliva test and 1743 comparing AI+ with the saliva test (with AI+ using digital consumer data, in addition to electronic medical data). Overall, only 36.68% of responses showed preferences for AI/AI+ alternatives. Stratified by AI sensitivity levels, acceptance rates for AI/AI+ were 35.04% at 60% sensitivity and 31.63% at 70% sensitivity, and increased markedly to 48.68% at 95% sensitivity (p <0.01). Similarly, acceptance rates by specificity were 29.68%, 28.18%, and 44.24% at 60%, 70%, and 95% specificity, respectively (P < 0.01). Notably, AI consistently garnered higher acceptance rates (45.82%) than AI+ (28.92%) at comparable sensitivity and specificity levels, except at 60% sensitivity, where no significant difference was observed. These results highlight the nuanced preferences for AI diagnostics, with higher sensitivity and specificity significantly driving acceptance of AI diagnostics.

Suggested Citation

  • Yulin Hswen & Ismaël Rafaï & Antoine Lacombe & Bérengère Davin-Casalena & Dimitri Dubois & Thierry Blayac & Bruno Ventelou, 2024. "Does improving diagnostic accuracy increase artificial intelligence adoption? A public acceptance survey using randomized scenarios of diagnostic methods," Post-Print hal-04746007, HAL.
    • Handle: RePEc:hal:journl:hal-04746007
    DOI: 10.36922/aih.3561
    Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-04746007v1
    as

    Download full text from publisher

    File URL: https://hal.inrae.fr/hal-04746007v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.36922/aih.3561?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
    ---><---

    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:hal:journl:hal-04746007. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.