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.

Read More:https://www.ajronline.org/doi/full/10.2214/AJR.19.21527

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.