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

Background: 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. Objective: 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). Methods: 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. Results: 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. Conclusions: 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.