โก Quick Summary
A recent study developed and validated the ATRAI-14 questionnaire to assess radiologists’ attitudes towards the implementation of Artificial Intelligence (AI) in radiology. The findings indicate that the questionnaire is a reliable tool for measuring AI acceptance, which is crucial for effective management decisions in the field.
๐ Key Details
- ๐ Sample Size: 90 radiologists participated in the pilot testing.
- ๐งฉ Domains Assessed: Familiarity, Trust, Implementation Perspective, Hopes and Fears.
- โ๏ธ Validation Methods: Cognitive interviews and confirmatory factor analysis (CFA).
- ๐ Reliability Metrics: Cronbach’s Alpha 0.78, ICC 0.89, Spearman’s rho 0.73.
๐ Key Takeaways
- ๐ ATRAI-14 is a newly developed questionnaire specifically for radiologists.
- ๐ก The study highlights the importance of understanding radiologists’ views on AI implementation.
- ๐ฉโ๐ฌ Cognitive interviews were conducted with 20 radiologists to refine the questionnaire.
- ๐ The questionnaire demonstrated acceptable internal consistency and good test-retest reliability.
- ๐ The four domains provide a comprehensive view of radiologists’ perspectives on AI.
- ๐ Criterion validity was confirmed, indicating the questionnaire’s effectiveness in measuring AI acceptance.
- ๐ The findings can guide management decisions regarding AI integration in radiology.
- ๐ฎ Future research could expand on these findings to enhance AI tools in clinical settings.
๐ Background
The integration of Artificial Intelligence (AI) into radiology is rapidly evolving, yet there remains a significant gap in understanding how radiologists perceive these technologies. Mixed acceptance levels and concerns about real-world applicability highlight the need for tools that can accurately gauge radiologists’ attitudes towards AI. The development of the ATRAI-14 questionnaire aims to fill this gap, providing insights that can inform the implementation of AI in clinical practice.
๐๏ธ Study
The study involved the creation of the ATRAI-14 questionnaire, which was based on items from the European Society of Radiology questionnaire. After an item reduction process, 23 items were identified, with 12 contributing to scoring across four key domains: Familiarity, Trust, Implementation Perspective, and Hopes and Fears. The questionnaire underwent cognitive interviews and was pilot tested with a representative sample of 90 radiologists to assess its reliability and validity.
๐ Results
The confirmatory factor analysis (CFA) confirmed the feasibility of the four-domain structure of the ATRAI-14 questionnaire. The results showed an acceptable internal consistency with a Cronbach’s Alpha of 0.78, good test-retest reliability with an ICC of 0.89, and acceptable criterion validity with a Spearman’s rho of 0.73. These metrics indicate that the questionnaire is a robust tool for measuring radiologists’ acceptance of AI.
๐ Impact and Implications
The development of the ATRAI-14 questionnaire has significant implications for the field of radiology. By providing a structured approach to assess radiologists’ attitudes towards AI, this tool can facilitate better management decisions regarding AI implementation. Understanding these perspectives is crucial for overcoming barriers to AI adoption and ensuring that these technologies are effectively integrated into clinical workflows, ultimately enhancing patient care.
๐ฎ Conclusion
The ATRAI-14 questionnaire represents a significant advancement in understanding radiologists’ views on AI in radiology. Its validation demonstrates its potential as a reliable tool for measuring AI acceptance, which is essential for informed decision-making in the implementation of AI technologies. As the field continues to evolve, further research will be vital in exploring the full impact of AI on radiology and patient outcomes.
๐ฌ Your comments
What are your thoughts on the integration of AI in radiology? Do you believe tools like the ATRAI-14 questionnaire can help bridge the gap between technology and clinical practice? Letโs start a conversation! ๐ฌ Leave your thoughts in the comments below or connect with us on social media:
Development and Validation of a Questionnaire to Assess the Radiologists’ Views on the Implementation of Artificial Intelligence in Radiology (ATRAI-14).
Abstract
Introduction: 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). Materials and Methods: 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). Results: 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], p-value < 0.05), and acceptable criterion validity (Spearman’s rho 0.73, p-value < 0.001). Conclusions: The questionnaire is useful for providing detailed AI acceptance measurements for making management decisions when implementing AI in radiology.
Author: [‘Vasilev YA’, ‘Vladzymyrskyy AV’, ‘Alymova YA’, ‘Akhmedzyanova DA’, ‘Blokhin IA’, ‘Romanenko MO’, ‘Seradzhi SR’, ‘Suchilova MM’, ‘Shumskaya YF’, ‘Reshetnikov RV’]
Journal: Healthcare (Basel)
Citation: Vasilev YA, et al. Development and Validation of a Questionnaire to Assess the Radiologists’ Views on the Implementation of Artificial Intelligence in Radiology (ATRAI-14). Development and Validation of a Questionnaire to Assess the Radiologists’ Views on the Implementation of Artificial Intelligence in Radiology (ATRAI-14). 2024; 12:(unknown pages). doi: 10.3390/healthcare12192011