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

Yulin Hswen (), Ismaël Rafaï (), Antoine Lacombe (), Bérengère Davin-Casalena, Dimitri Dubois (), Thierry Blayac and Bruno Ventelou
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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
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
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

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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.

Keywords: Artificial intelligence; AI diagnostics; Neurodegenerative diseases; Machine learning (search for similar items in EconPapers)
Date: 2024-10-18
New Economics Papers: this item is included in nep-exp
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Published in Artificial Intelligence in Health, 2024, ⟨10.36922/aih.3561⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04746007

DOI: 10.36922/aih.3561

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