Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence

Insights into Imaging

Table 5 Logistic regression analyses on the association of increased workload with a study’s research area and impact factor of the journal in which the study was published, for a non-academic general teaching hospital

Variable	Univariate analysis			Multivariate analysis
Variable	OR	95% CI	p value	OR	95% CI	p value
Study’s research area^a	11.05^b	3.39–36.01^b	< 0.001^b	10.45^b	3.19–34.21^b	< 0.001^b
Impact factor of the journal in which the study was published^c	0.94^d	0.87–1.01^d	0.065	0.950^d	0.87–1.04^d	0.268

CI: confidence interval, OR: odds ratio
^aBased on 277 studies with the following primary research areas: artificial intelligence (n = 37), breast (n = 16), cardiac (n = 32), chest (n = 18), computed tomography (n = 4), emergency (n = 1), gastrointestinal–abdominal (n = 20), head–neck (n = 6), magnetic resonance (n = 3), multisystem (n = 1), musculoskeletal (n = 18), neuroradiology (n = 44), nuclear medicine (n = 50), oncology (n = 1), pediatric (n = 2), ultrasonography (n = 2), urogenital (n = 17), and vascular (n = 5)
^bStudies with artificial intelligence as research area were significantly associated with increased workload
^cBased on 25 individual journals with a median impact factor of 4.966 (range 2.687–33.752)
^dPer unit increase in impact factor