Liu Yongmei, Wang Zichun, Peng Bo
Business School, Central South University, Xiaoxiang Middle Road, Jiangwan Building, Xiaoxiang Campus of Central South University, Changsha, 410083, China, 86 18419221269.
Urban Smart Governance Laboratory, Central South University, Changsha, China.
J Med Internet Res. 2025 Aug 19;27:e62885. doi: 10.2196/62885.
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.
能够进行独立诊断的人工智能（AI）系统的开发为优化医疗资源分配提供了一个有前景的解决方案，尤其是因为它们的诊断准确性可能超过一些初级医务人员。然而，尽管有这些进展，许多患者对接受AI技术仍表现出犹豫，特别是对于自主诊断的角色。AI医生呈现的信息质量影响患者采用其进行独立诊断意愿的机制仍不清楚。
本研究旨在探讨AI医生的信息质量如何影响患者采用其进行独立诊断的意愿。具体而言，借鉴精细加工可能性模型，本研究试图了解诊断透明度（DT）和诊断论据质量（DAQ；作为AI提供信息的方面）如何影响患者采用人工智能医生进行独立诊断（IAID）的意愿，这些影响通过感知专业知识（PE）和认知信任（CT）进行中介。
进行了一项基于情景的实验，以研究信息质量对患者采用意愿的影响。为了检验假设，采用了2（DT：低或高）×2（DAQ：低或高）组间实验设计。每个实验组由60名有效参与者组成，共获得240个有效回复。使用双向方差分析和偏最小二乘法对数据进行分析。
DT（β = 0.157；P = 0.008）和DAQ（β = 0.444；P < 0.001）均对患者的PE有显著正向影响。作为中心路径，DAQ实验操纵（均值1 4.55，标准差1.40；均值2 5.68，标准差0.81；F1,236 = 59.701；P < 0.001；ηp2 = 0.202）对PE的影响比DT（均值1 4.92，标准差1.24；均值2 5.31，标准差1.28；F1,236 = 7.303；P = 0.007；ηp2 = 0.030）更显著。同时，PE对CT有正向影响（β = 0.845；P < 0.001），CT也对患者的IAID有正向影响（β = 0.679；P < 0.001）。通过PE和CT的串行中介路径完全中介了DT（β = 0.090；95%CI 0.017 - 0.166）和DAQ（β = 0.254；95%CI 0.193 - 0.316）对患者IAID的影响。
DAQ（中心线索）和DT（外周线索）影响患者的IAID。这些影响通过一个顺序路径完全中介：两个线索都增强了PE，其中DAQ的作用比DT显著更强，这反过来又促进了CT，随后塑造了IAID。实际上，这些结果突出表明，为了促进患者采用，应优先努力提高AI诊断论据的质量和清晰度，因为这条路径能更有力地建立PE，进而建立CT。这一见解对于设计患者会认为可接受且值得信赖以承担各种诊断职责的AI医生至关重要。
