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 Journal of Imaging Informatics in Medicine, Volume 39, Issue 4, 2026, Pages 3592 - 3600.

The published version is available at https://doi.org/10.1007/s10278-025-01485-8. 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 200.
https://jdc.jefferson.edu/radiologyfp/200

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

PubMed ID

41032170

Language

English