Table 2.

- Artificial intelligence applications in radiology corresponding to gender, training level and familiar with big data.

Questions/answers	n (%)	P-value
		Gender	Training level	Big data
Should radiologists be educated on (choose at most 3):
Clinical use of AI applications	117 (76)
Advantages and limitations of AI applications	115 (74.7)
Technical methods, such as machine/deep learning algorithm	63 (40.9)
How to get into the driver seat in using AI	62 (40.3)
How to survive the AI revolution	29 (18.8)
How to avoid the use of AI applications	11 (7.1)
What are the advantages of using AI? (choose at most 2)
AI can speed up processes in health care	122 (79.2)	0.257	0.468	0.748
AI can help reduce medical errors	73 (47.4)	0.298	0.734	0.784
AI has no emotional exhaustion nor physical limitation	43 (27.9)	1.00	0.190	0.618
AI can deliver vast amounts of clinically relevant high-quality data in real time	27 (17.5)	1.00	0.440‡	0.582
AI has no space-time constraint	16 (10.4)	0.860	0.118‡	1.00
What are you concerned about regarding the application of AI in medicine?
It cannot be used to provide opinions in unpredicted situations due to inadequate information	59 (38.3)
It is not flexible enough to be applied to every patient	53 (34.4)
It is difficult to apply to controversial subjects	21 (13.6)	0.193‡	0.198‡	0.968‡
The low ability to sympathize and consider the emotional well-being of the patient	11 (7.1)
It was developed by a less experienced medical clinician	10 (6.5)
Are you involved in any research project based on AI-based application development
Yes, testing	5 (3.2)
Yes, developing	10 (6.5)	0.485‡	0.774‡	0.027‡
No, but planning to be involved	44 (28.6)
No	95 (61.7)
Would you be willing to help in learning about ML and training a ML algorithm so that it can imitate some of the tasks you perform as a radiologist?
Yes	120 (77.9)	0.096	0.049	0.026
No	34 (22.1)

AI: artificial intelligence, ML: machine learning, ^‡Fisher’s exact test