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

Insights into Imaging

Table 4 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 an academic tertiary care center

Variable	Univariate analysis			Multivariate analysis
Variable	OR	95% CI	p value	OR	95% CI	p value
Study’s research area^a	11.79^b	3.64–38.28^b	< 0.001^b	10.64^b	3.25–34.80^b	< 0.001^b
Impact factor of the journal in which the study was published^c	0.93^d	0.86–1.00^d	0.020	0.93^d	0.85–1.02^d	0.110

CI: confidence interval, OR: odds ratio
^aBased on 286 studies with the following primary research areas: artificial intelligence (n = 38), breast (n = 16), cardiac (n = 32), chest (n = 20), computed tomography (n = 4), emergency (n = 1), gastrointestinal–abdominal (n = 21), head–neck (n = 6), magnetic resonance (n = 3), multisystem (n = 1), musculoskeletal (n = 18), neuroradiology (n = 46), nuclear medicine (n = 51), oncology (n = 2), pediatric (n = 2), ultrasonography (n = 2), urogenital (n = 17), and vascular (n = 6)
^bStudies with artificial intelligence as research area were significantly associated with increased workload
^cBased on 26 individual journals with a median impact factor of 5.061 (range: 2.687–33.752)
^dPer unit increase in impact factor