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To compare the motivation, deterrents, knowledge, exposure, and other specialty considerations of first- to fourth-year medical students interested in interventional radiology (IR) with those who are not.Matriculants of 5 medical schools varying by region, public/private, class size, and National Institutes of Health research ranking received a 19-question survey with questions about demographics, specialty interests, motivations/deterrents, knowledge, and exposure to IR.A total of 25.8% (611/2370) of students completed the survey, of which 20.5% (125/611) expressed interest in IR, and 25% (47/186), 26% (40/153), 24% (34/143), and 3% (3/117) of first-year, second-year, third-year, and fourth-year medical students, respectively, were seriously considering IR. Those interested in IR were less motivated by direct patient care (mean, 2.8/5; 95% confidence interval [CI], 2.6-3.0) and longitudinal patient care (mean, 1.6/5; 95% CI, 1.4-1.7) (both, P.01) and more motivated by salary (2.6/5; 95% CI, 2.3-2.9), job market (2.8/5; 95% CI, 2.6-2.9), and procedures (3.1/5; 95% CI, 2.8-3.4) compared with their peers (all P.05). Those interested in IR were more certain about their IR knowledge (mean range, 1.6-2.0/3.0; 95% CI, 1.3-2.3) than their peers (mean range, 1.9-2.4/3.0; 95% CI, 1.6-2.1, in which 0 = certain, P ≤ .01); however, both groups scored low in actual knowledge (those considering IR: 35.0-73.2% correct; 95% CI, 23.5-81.4; those who were not: 26.6-66.7% correct; 95% CI, 24.3-75.9, P.05).Although medical students showed interest in IR, they had a limited understanding of IR. IR educators may increase IR interest and understanding among medical students by clarifying the procedural aspects and longitudinal care present in a comprehensive IR practice.

Purpose To demonstrate that random forest models trained on a large national sample can accurately predict relevant outcomes and may ultimately contribute to future clinical decision support tools in IR. Materials and Methods Patient data from years 2012–2014 of the National Inpatient Sample were used to develop random forest machine learning models to predict iatrogenic pneumothorax after computed tomography–guided transthoracic biopsy (TTB), in-hospital mortality after transjugular intrahepatic portosystemic shunt (TIPS), and length of stay > 3 days after uterine artery embolization (UAE). Model performance was evaluated with area under the receiver operating characteristic curve (AUROC) and maximum F1 score. The threshold for AUROC significance was set at 0.75. Results AUROC was 0.913 for the TTB model, 0.788 for the TIPS model, and 0.879 for the UAE model. Maximum F1 score was 0.532 for the TTB model, 0.357 for the TIPS model, and 0.700 for the UAE model. The TTB model had the highest AUROC, while the UAE model had the highest F1 score. All models met the criteria for AUROC significance. Conclusions This study demonstrates that machine learning models may suitably predict a variety of different clinically relevant outcomes, including procedure-specific complications, mortality, and length of stay. Performance of these models will improve as more high-quality IR data become available.