Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Apr 17, 2023
Date Accepted: Sep 1, 2023
Ready to embrace artificial intelligence? Results of a nationwide survey on radiology residents in China: A Cross-Sectional Study
ABSTRACT
Background:
Artificial intelligence (AI) has been transforming the world, and a key but controversial battleground of AI applications is health care, particularly in diagnostic specialties such as radiology. However, few studies systematically explore the response of ‘human intelligence’ to AI.
Objective:
The aims of this study are to understand their perceived replacement, usefulness and acceptance of AI, and identify antecedent factors. We consider the impact of a wide range of factors including demographic characteristics, working status, psychosocial aspects, personal experience, and contextual factors.
Methods:
A total of 3666 radiology residents from China completed a cross-sectional survey between 1 December 2020 and 30 April 2021. Multivariable logistic regression models are adopted also to examine various factors and associations. Odds ratios and 95% confidence interval were reported.
Results:
Overall, radiology residents have a positive attitude towards AI, with 29.89% agreeing that AI would reduce the demand for radiologists, 72.80% believing AI improves disease diagnosis, and finally 78.18% believing radiologists should embrace AI. Many associated factors such as age, gender, education, eye strain status, working hours, time spent on medical images, resilience, burnout, the experience of hearing of AI, the experience of using AI, the perceived residency support, and the perceived residency stress have been found to have significant impacts on the attitudes towards AI. For instance, those with burnout symptoms are more concerned with being replaced by AI (OR = 1.86, P <.001), less favourable to AI usefulness (OR = 0.77, P=.005), and less expected to use AI (OR = 0.71, P<.001). In addition, adjusting for all other factors, two opposite AI perceptions, perceived AI replacement (OR = 0.81, P<.001) and AI usefulness (OR = 5.97, P<.001), have been proved to significantly influence the intention to use AI, respectively.
Conclusions:
This study characterises radiology residents who embrace AI. Our rich findings inform a multidimensional approach for physicians to adapting to AI. Targeted policies in terms of digital healthcare initiatives, medical education, etc., can be developed accordingly.
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