TangATamRCadrin-ChênevertA, et al. Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J. 2018;69(2):120-135. doi:10.1016/j.carj.2018.02.002
2.
BriodyHHannemanKPatlasMN. Applications of artificial intelligence in acute thoracic imaging. Can Assoc Radiol J. Published online February 19, 2025. doi:10.1177/08465371251322705
3.
SorinVSofferSGlicksbergBSBarashYKonenEKlangE. Adversarial attacks in radiology – a systematic review. Eur J Radiol. 2023;167:111085. doi:10.1016/j.ejrad.2023.111085
4.
EykholtKEvtimovIFernandesE, et al. Robust physical-world attacks on deep learning visual classification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; June 18-23, 2018; Salt Lake City, UT. doi:10.1109/CVPR.2018.00175
5.
ChenYEsmaeilzadehP. Generative AI in medical practice: in-depth exploration of privacy and security challenges. J Med Internet Res. 2024;26(1):e53008. doi:10.2196/53008
6.
BradyAPAllenBChongJ, et al. Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. Can Assoc Radiol J. 2024;75(2):226-244. doi:10.1177/08465371231222229
7.
SchneierB. Secrets and Lies. Wiley; 2015. doi:10.1002/9781119183631
