糖尿病患者接受人工智能治疗糖尿病视网膜病变的SEM模型分析。

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Luchang Jin, Yanmin Tao, Ya Liu, Gang Liu, Lin Lin, Zixi Chen, Sihan Peng
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引用次数: 0

摘要

目的:本研究旨在了解糖尿病患者对人工智能(AI)设备用于糖尿病视网膜病变筛查的接受程度及其影响因素。方法:构建综合模型,采用结构方程模型对项目进行评价,并通过验证性因子分析构建信度和效度。分析了模型的路径效应、显著性、拟合优度以及中介和调节效应。结果:主观规范(SN)、抵抗偏差(RB)和唯一性忽视(UN)显著影响使用意向(IU)。感知有用性(PU)和感知易用性(PEOU)是IU与其他变量之间的显著中介。信任(TR)对PU到IU的路径的调节作用不显著。结论:中国的集体主义和威权主义文化可能会对学生学习产生显著的积极影响。PU和PEOU均具有显著的中介效应,表明印象影响接受度。虽然TR的调节作用不显著,但本研究的非标准化因子负荷仍为正。我们认为这可能是由于样本数量不足,公众对人工智能医疗设备不熟悉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SEM model analysis of diabetic patients' acceptance of artificial intelligence for diabetic retinopathy.

Aims: This study aimed to investigate diabetic patients' acceptance of artificial intelligence (AI) devices for diabetic retinopathy screening and the related influencing factors.

Methods: An integrated model was proposed, and structural equation modeling was used to evaluate items and construct reliability and validity via confirmatory factor analysis. The model's path effects, significance, goodness of fit, and mediation and moderation effects were analyzed.

Results: Intention to Use (IU) is significantly affected by Subjective Norms (SN), Resistance Bias (RB), and Uniqueness Neglect (UN). Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) were significant mediators between IU and other variables. The moderating effect of trust (TR) is non-significant on the path of PU to IU.

Conclusions: The significant positive impact of SN may be caused by China's collectivist and authoritarian cultures. Both PU and PEOU had a significant mediation effect, which suggests that impressions influence acceptance. Although the moderating effect of TR was not significant, the unstandardized factor loading remained positive in this study. We presume that this may be due to an insufficient sample size, and the public was unfamiliar with AI medical devices.

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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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