Development and Validation of a Questionnaire to Assess the Radiologists’ Views on the Implementation of Artificial Intelligence in Radiology (ATRAI-14)

Abstract

<b>Introduction</b>: 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). <b>Materials and Methods</b>: 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). <b>Results</b>: 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], <i>p</i>-value < 0.05), and acceptable criterion validity (Spearman's rho 0.73, <i>p</i>-value < 0.001). <b>Conclusions</b>: The questionnaire is useful for providing detailed AI acceptance measurements for making management decisions when implementing AI in radiology.

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