Radyoloji Asistanları Arasında Türkçe ATRAI-14'ün Kültürlerarası Uyarlaması ve Psikometrik Değerlendirmesi: Çok Merkezli Ulusal Bir Anket

Özet (abstract)

Artificial intelligence (AI) is increasingly integrated into radiology, and residents’ attitudes are central to its adoption. The ATRAI-14 measures radiologists’ attitudes toward AI implementation, but its performance in other linguistic and health-system contexts is unknown. We aimed to adapt it into Turkish, examine whether its three-domain structure replicates in a national cohort of radiology residents, and describe their attitudes. We surveyed radiology residents from 25 institutions across 17 cities in Türkiye in a national, cross-sectional, web-based study. The questionnaire was translated into Turkish and content-validated by eight expert radiologists (item-content validity index 0.875). Psychometric evaluation comprised confirmatory and exploratory factor analysis, Cronbach’s α and McDonald’s ω, item–total correlations, and criterion validity against a visual analog scale. Group differences and predictors of the total score were examined non-parametrically and by regression. Among 286 participants, the three-factor model fitted inadequately (CFI = 0.82, RMSEA = 0.075). Only Trust reached borderline-acceptable internal consistency (α = 0.69); Implementation Perspectives (α = 0.07) and Hopes and Fears (α = 0.06) lacked cohesion, and two items loaded negatively. Criterion validity for the total score was acceptable (ρ = 0.56), and the mean total score (20.99 ± 3.60) was 2.99 points above the scale midpoint of 18, indicating a modestly positive attitude. Residents favored AI as a collaborative aid (90.6% would re-examine a study when AI disagreed; item T4) but were uncomfortable with autonomous AI (22.0%; item T1). Most expected reduced workload (79.7%; item H4), yet 60.8% feared diminished prestige (item H1), and only 21.7% had access to AI tools at work. Attitudes did not differ by AI access, seniority, institution type, or research involvement. The three-domain structure only partially replicated: Trust performed acceptably, whereas the other two require cross-cultural refinement before the total score can be used reliably. Because these two domains did not form coherent constructs, their findings should be read as observations about individual items rather than as measurements of the intended constructs, and should not be used to compare groups. With that caveat, the cohort was cautiously optimistic, valuing AI as a collaborator while resisting its autonomy. Few residents had used AI in practice; incorporating supervised, hands-on AI experience into residency training could help align expectations with practice — a hypothesis warranting direct evaluation rather than a conclusion from cross-sectional data.

Yazarların özeti; kaynağından alınmıştır. BMC Medical Education, 2026 · DOI ↗