Skip to main content

Table 1 Multiple-choice questions related to respondent’s age, sex, radiology subspecialty, most frequently practiced techniques and working status, type of institution, and country

From: Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology

Question number Topic Answers
Maximum number List
I Status 1 Medical student, Resident, Radiologist, Engineer/Computer scientist, Physicist, Other
II Working place 1 University/Teaching hospital, Hospital, Private practice, Private research centre, Private company, Other
III Gender 1 Male, Female
IV Age range 1 18–29 years; 30–39 years; 40–49 years; 50–59 years; 60–69 years; ≥ 70 years
V Home country 1 Albania; Austria; Armenia; Belarus; Belgium; Bosnia and Herzegovina; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Georgia; Germany; Greece; Hungary; Iceland; Ireland; Israel; Italy; Kazakhstan; Kosovo; Kyrgyzstan; Latvia; Lithuania; Luxembourg; Macedonia; Malta; Montenegro; Netherlands; Norway; Poland; Portugal; Romania; Russia; Serbia; Slovakia; Slovenia; Spain; Sweden; Switzerland; Turkey; Ukraine; United Kingdom; Uzbekistan
VI Subspecialty 5 Breast; Cardiovascular; Emergency; Gastrointestinal/Abdominal; General; Head & Neck; Interventional; Molecular imaging/Nuclear; Musculoskeletal; Neuroradiology; Oncologic imaging; Paediatric; Thoracic; Urogenital
VII Practiced techniques 5 Radiography; Mammography; Ultrasound; Angiography/Fluoroscopy; CT; MRI; PET/Nuclear; Hybrid imaging; DXA; Experimental imaging (animal models); Optical imaging; Other