Artificial Intelligence-Powered Clinical Decision Support and Simulation Platform for Radiology Trainee Education
Author:
Funder
Galileo CDS, Inc
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Link
https://link.springer.com/content/pdf/10.1007/s10278-022-00713-9.pdf
Reference16 articles.
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3. O. A. Awan, F. Shaikh, B. Kalbfleisch, E. L. Siegel, and P. Chang, “RSNA Diagnosis Live: A Novel Web-based Audience Response Tool to Promote Evidence-based Learning.,” Radiographics, vol. 37, no. 4, pp. 1111–1118, Jul. 2017, https://doi.org/10.1148/rg.2017160107.
4. E. I. Rubio, M. J. Bassignani, M. A. White, and W. E. Brant, “Effect of an audience response system on resident learning and retention of lecture material,” Am. J. Roentgenol., vol. 190, no. 6, Jun. 2008, https://doi.org/10.2214/AJR.07.3038.
5. M. T. Duong et al., “Artificial intelligence for precision education in radiology.,” Br. J. Radiol., vol. 92, no. 1103, p. 20190389, Nov. 2019, https://doi.org/10.1259/bjr.20190389.
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