Vasilev Yuriy A, Vladzymyrskyy Anton V, Alymova Yulya A, Akhmedzyanova Dina A, Blokhin Ivan A, Romanenko Maria O, Seradzhi Seal R, Suchilova Maria M, Shumskaya Yuliya F, Reshetnikov Roman V
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051 Moscow, Russia.
Healthcare (Basel). 2024 Oct 9;12(19):2011. doi: 10.3390/healthcare12192011.
: Artificial Intelligence (AI) is becoming an essential part of modern radiology. However, available evidence highlights issues in the real-world applicability of AI tools and mixed radiologists' acceptance. We aimed to develop and validate a questionnaire to evaluate the attitude of radiologists toward radiology AI (ATRAI-14). : We generated items based on the European Society of Radiology questionnaire. Item reduction yielded 23 items, 12 of which contribute to scoring. The items were allocated into four domains ("Familiarity", "Trust", "Implementation Perspective", and "Hopes and Fears") and a part related to the respondent's demographics and professional background. As a pre-test method, we conducted cognitive interviews with 20 radiologists. Pilot testing with reliability and validity assessment was carried out on a representative sample of 90 respondents. Construct validity was assessed via confirmatory factor analysis (CFA). : CFA confirmed the feasibility of four domains structure. ATRAI-14 demonstrated acceptable internal consistency (Cronbach's Alpha 0.78 95%CI [0.68, 0.83]), good test-retest reliability (ICC = 0.89, 95% CI [0.67, 0.96], -value < 0.05), and acceptable criterion validity (Spearman's rho 0.73, -value < 0.001). : The questionnaire is useful for providing detailed AI acceptance measurements for making management decisions when implementing AI in radiology.
人工智能(AI)正成为现代放射学的重要组成部分。然而,现有证据凸显了人工智能工具在现实世界中的适用性问题以及放射科医生的复杂接受度。我们旨在开发并验证一份问卷,以评估放射科医生对放射学人工智能的态度(ATRAI - 14)。
我们基于欧洲放射学会的问卷生成条目。经过条目删减得到23个条目,其中12个用于计分。这些条目被分配到四个领域(“熟悉程度”“信任度”“实施视角”以及“希望与担忧”)以及一部分与受访者人口统计学和专业背景相关的内容。作为一种预测试方法,我们对20名放射科医生进行了认知访谈。对90名具有代表性的受访者样本进行了可靠性和有效性评估的预试验。通过验证性因子分析(CFA)评估结构效度。
CFA证实了四个领域结构的可行性。ATRAI - 14表现出可接受的内部一致性(克朗巴哈系数α为0.78,95%置信区间[0.68, 0.83])、良好的重测信度(组内相关系数ICC = 0.89,95%置信区间[0.67, 0.96],p值<0.05)以及可接受的效标效度(斯皮尔曼等级相关系数rho为0.73,p值<0.001)。
该问卷有助于在放射学中实施人工智能时提供详细的人工智能接受度测量,以用于做出管理决策。