Healthcare Innovations and Translational Informatics lab (HITI) @ Emory


Machine learning for Health, Imaging Informatics

Machine Learning for the Interventional Radiologist


Journal article


Ryan D. Meek, Matthew P. Lungren, Judy W. Gichoya
American Journal of Roentgenology, vol. 213(4), 2019 Aug 30, pp. 782-784

OBJECTIVE. The purpose of this article is to describe key potential areas of application of machine learning in interventional radiology.
CONCLUSION. Machine learning, although in the early stages of development within the field of interventional radiology, has great potential to influence key areas such as image analysis, clinical predictive modeling, and trainee education. A proactive approach from current interventional radiologists and trainees is needed to shape future directions for machine learning and artificial intelligence.

Cite

APA
Meek, R. D., Lungren, M. P., & Gichoya, J. W. (2019). Machine Learning for the Interventional Radiologist. American Journal of Roentgenology, 213(4), 782–784.

Chicago/Turabian
Meek, Ryan D., Matthew P. Lungren, and Judy W. Gichoya. “Machine Learning for the Interventional Radiologist.” American Journal of Roentgenology 213, no. 4 (August 30, 2019): 782–784.

MLA
Meek, Ryan D., et al. “Machine Learning for the Interventional Radiologist.” American Journal of Roentgenology, vol. 213, no. 4, Aug. 2019, pp. 782–84.