Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM

Authors

Felipe Kitamura
Timothy Kline
Daniel Warren
Linda Moy
Roxana Daneshjou
Farhad Maleki
Igor Santos
Judy Gichoya
Walter Wiggins
Brian Bialecki
Kevin O'Donnell
Adam E. Flanders, Thomas Jefferson UniversityFollow
Matt Morgan
Nabile Safdar
Katherine P. Andriole
Raym Geis
Bibb Allen
Keith Dreyer
Matt Lungren
Monica J. Wood
Marc Kohli
Steve Langer
George Shih
Eduardo Farina
Charles E. Kahn
Ingrid Reiser
Maryellen Giger
Christoph Wald
John Mongan
Tessa Cook
Neil Tenenholtz

Document Type

Article

Publication Date

10-1-2025

Comments

This article is the author's final published version in Radiology: Artificial Intelligence, Volume 7, Issue 6, November 2025, Article number e250137.

The published version is available at https://doi.org/10.1148/ryai.250137. Copyright © The Author(s) 2025.

Abstract

No abstract available

Recommended Citation

Kitamura, Felipe; Kline, Timothy; Warren, Daniel; Moy, Linda; Daneshjou, Roxana; Maleki, Farhad; Santos, Igor; Gichoya, Judy; Wiggins, Walter; Bialecki, Brian; O'Donnell, Kevin; Flanders, Adam E.; Morgan, Matt; Safdar, Nabile; Andriole, Katherine P.; Geis, Raym; Allen, Bibb; Dreyer, Keith; Lungren, Matt; Wood, Monica J.; Kohli, Marc; Langer, Steve; Shih, George; Farina, Eduardo; Kahn, Charles E.; Reiser, Ingrid; Giger, Maryellen; Wald, Christoph; Mongan, John; Cook, Tessa; and Tenenholtz, Neil, "Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM" (2025). Department of Radiology Faculty Papers. Paper 188.
https://jdc.jefferson.edu/radiologyfp/188

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

PubMed ID

41031949

Language

English