TY  - JOUR
AU  - Chen, Yanhua
AU  - Wu, Ziye
AU  - Wang, Peicheng
AU  - Xie, Linbo
AU  - Yan, Mengsha
AU  - Jiang, Maoqing
AU  - Yang, Zhenghan
AU  - Zheng, Jianjun
AU  - Zhang, Jingfeng
AU  - Zhu, Jiming
PY  - 2023
DA  - 2023/10/19
TI  - Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study
JO  - J Med Internet Res
SP  - e48249
VL  - 25
KW  - artificial intelligence
KW  - technology acceptance
KW  - radiology
KW  - residency
KW  - perceptions
KW  - health care services
KW  - resident
KW  - residents
KW  - perception
KW  - adoption
KW  - readiness
KW  - acceptance
KW  - cross sectional
KW  - survey
AB  - Background: Artificial intelligence (AI) is transforming various fields, with health care, especially diagnostic specialties such as radiology, being a key but controversial battleground. However, there is limited research systematically examining the response of “human intelligence” to AI. Objective: This study aims to comprehend radiologists’ perceptions regarding AI, including their views on its potential to replace them, its usefulness, and their willingness to accept it. We examine the influence of various factors, encompassing demographic characteristics, working status, psychosocial aspects, personal experience, and contextual factors. Methods: Between December 1, 2020, and April 30, 2021, a cross-sectional survey was completed by 3666 radiology residents in China. We used multivariable logistic regression models to examine factors and associations, reporting odds ratios (ORs) and 95% CIs. Results: In summary, radiology residents generally hold a positive attitude toward AI, with 29.90% (1096/3666) agreeing that AI may reduce the demand for radiologists, 72.80% (2669/3666) believing AI improves disease diagnosis, and 78.18% (2866/3666) feeling that radiologists should embrace AI. Several associated factors, including age, gender, education, region, eye strain, working hours, time spent on medical images, resilience, burnout, AI experience, and perceptions of residency support and stress, significantly influence AI attitudes. For instance, burnout symptoms were associated with greater concerns about AI replacement (OR 1.89; P<.001), less favorable views on AI usefulness (OR 0.77; P=.005), and reduced willingness to use AI (OR 0.71; P<.001). Moreover, after adjusting for all other factors, perceived AI replacement (OR 0.81; P<.001) and AI usefulness (OR 5.97; P<.001) were shown to significantly impact the intention to use AI. Conclusions: This study profiles radiology residents who are accepting of AI. Our comprehensive findings provide insights for a multidimensional approach to help physicians adapt to AI. Targeted policies, such as digital health care initiatives and medical education, can be developed accordingly. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e48249
UR  - https://doi.org/10.2196/48249
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37856181
DO  - 10.2196/48249
ID  - info:doi/10.2196/48249
ER  -