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

    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, 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 Jr, Ingrid Reiser, Maryellen Giger, Christoph Wald, John Mongan, Tessa Cook, Neil Tenenholtz
    J Imaging Inform Med. 2025 Oct 1. doi: 10.1007/s10278-025-01485-8. Online ahead of print.

    Medical imaging is undergoing a transformation driven by the advent of new, highly effective, machine learning techniques paired with increases in computational capabilities (Cheng et al. [3]; [1, 8, 12]).