Understanding the Impact of AI Doctors’ Information Quality on Patients’ Intentions to Adopt AI for Independent Diagnosis: Scenario-Based Experimental Study

The development of artificial intelligence (AI) systems capable of independent diagnosis offers a promising solution for optimizing medical resource allocation, especially as their diagnostic accuracy can exceed that of some primary medical staff. However, despite these advancements, many patients e...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of medical Internet research Ročník 27; číslo 11; s. e62885
Hlavní autori: Liu, Yongmei, Wang, Zichun, Peng, Bo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Canada Journal of Medical Internet Research 19.08.2025
JMIR Publications
Predmet:
Adoption and Change Management of eHealth Systems
Adult
AI Language Models in Health Care
Applications of AI
Artificial Intelligence
Consumer Health Informatics Innovations
Decision Support for Health Professionals
Female
Humans
Information management
Intention
Machine Learning
Male
Mediation
Medical care
Medical personnel
Middle Aged
Original Paper
Physician-Patient Relations
Physicians
Quality management
Responsible Health AI
United States
Connecticut
information quality
AI doctor diagnosis system
digital consultation
diagnostic transparency
diagnostic argument quality
AI technology
artificial intelligence
digital health
decision-making
perceived expertise
scenario-based
disease diagnosis
health informatics
AI doctor
adoption intention
ISSN:1438-8871, 1439-4456, 1438-8871
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The development of artificial intelligence (AI) systems capable of independent diagnosis offers a promising solution for optimizing medical resource allocation, especially as their diagnostic accuracy can exceed that of some primary medical staff. However, despite these advancements, many patients exhibit hesitancy toward accepting AI technology, particularly for autonomous diagnostic roles. The mechanisms through which the information quality presented by AI doctors influences patients' intention to adopt them for independent diagnosis remain unclear. This study aimed to examine how the information quality of AI doctors influences patients' intentions to adopt them for independent diagnosis. Specifically, drawing on the elaboration likelihood model, this study seeks to understand how diagnostic transparency (DT) and diagnostic argument quality (DAQ; as aspects of AI-delivered information) affect patients' intention to adopt artificial intelligence doctors for independent diagnosis (IAID), with these effects being mediated by perceived expertise (PE) and cognitive trust (CT). A scenario-based experiment was conducted to investigate the impact of information quality on patients' adoption intentions. To test the hypotheses, a 2 (DT: low or high)×2 (DAQ: low or high) between-groups experimental design was used. Each experimental group consisted of 60 valid participants, yielding a total of 240 valid responses. Data were analyzed using 2-way ANOVA and partial least squares. Both DT (β=.157; P=.008) and DAQ (β=.444; P<.001) significantly positively affected patients' PE. As the central route, the influence of the experimental manipulation of DAQ (mean1 4.55, SD 1.40; mean2 5.68, SD 0.81; F1,236=59.701; P<.001; ηp2=0.202) on PE is more significant than that of DT (mean1 4.92, SD 1.24; mean2 5.31, SD 1.28; F1,236=7.303; P=.007; ηp2=0.030). At the same time, PE has a positive impact on CT (β=.845; P<.001), and CT also positively affected patients' IAID (β=.679; P<.001). The serial mediation pathway via PE and CT fully mediated the effects of both DT (β=.090; 95% CI 0.017-0.166) and DAQ (β=.254; 95% CI 0.193-0.316) on patients' IAID. DAQ (central cue) and DT (peripheral cue) influenced patients' IAID. These effects were fully mediated through a sequential pathway: both cues enhanced PE-with DAQ exerting a significantly stronger effect than DT-which in turn fostered CT, subsequently shaping IAID. Practically, these results highlight that to foster patient adoption, efforts should prioritize enhancing the quality and clarity of AI's diagnostic arguments, as this pathway more strongly builds PE and, subsequently, CT. This insight is crucial for designing AI doctors that patients will find acceptable and trustworthy for various diagnostic responsibilities.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/62885