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BACKGROUND: The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current landscape of AI integration in radiology education. METHODS: The review process involved systematically searching four databases, including MEDLINE (Ovid), Embase (Ovid), PsychINFO (Ovid), and Scopus. Inclusion criteria focused on research that addresses the use of AI technologies in radiology education, including but not limited to, AI-assisted learning platforms, simulation tools, and automated assessment systems. This scoping review was registered on Open Science Framework using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) extension to scoping review. RESULTS: Of the 1081 search results, 9 studies met the inclusion criteria. Key findings indicate a diverse range of AI applications in radiology education, from personalized curriculum generation and diagnostic support tools to automated evaluation systems. The review highlights both the potential benefits, such as enhanced diagnostic accuracy, and the challenges, including technical limitations. CONCLUSION: The integration of AI into radiology education, which has significant potential to enhance outcomes and professional practice, requires overcoming existing challenges and ensuring that AI complements rather than replaces traditional methods, with future research needed on longitudinal studies to evaluate its long-term impact.
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BACKGROUND: There is little evidence to guide management of patients with acute leukemia and intracranial hemorrhage (ICH). Predictors of long-term outcome following ICH are unknown. STUDY DESIGN AND METHODS: This study included adult patients with acute leukemia and ICH over an 8-year period. The primary outcome was data regarding 90-day mortality. Secondary outcomes included data related to the proportion of patients receiving post-remission therapy and predictors of 90-day mortality. RESULTS: ICH occurred in 101 patients; 12 patients died within 72 hours. For the 89 others, 90-day mortality was 40%. Of 43 patients who received induction, 30 achieved remission and 26 received post-remission therapy. Older age (p = 0.03) and higher white count (p = 0.02) at the time of ICH were predictive of inferior survival. During 90-day follow-up, median platelet count was 37 x 109 /L (0-1526 x 109 /L). Lower platelet count during follow-up was predictive of 90-day mortality (p = <0.01). Twenty-one percent of platelet transfusions were provided when the platelet count was less than 10 x 109 /L, 54% between 10 and 29 x 109 /L, and 25% greater than 30 x 109 /L. New or progressive ICH occurred in 23 patients. There was no difference in the median platelet transfusion trigger between patients who had new or progressive ICH and those who did not. CONCLUSION: In patients with acute leukemia, survival following ICH is poor. Older age and higher white count is associated with increased mortality, perhaps reflecting higher risk disease. Following ICH in acute leukemia platelet transfusions do not appear to alter the risk of progressive bleeding or mortality.