BriodyHHannemanKPatlasMN. Applications of artificial intelligence in acute thoracic imaging. Can Assoc Radiol J. 2025;76(3):454-465. doi:10.1177/08465371251322705
2.
DietrichNStubbertB. Evaluating adherence to Canadian radiology guidelines for incidental hepatobiliary findings using RAG-enabled LLMs. Can Assoc Radiol J. Published online February 27, 2025. doi:10.1177/08465371251323124
3.
DooFXVosshenrichJCookTS, et al. Environmental sustainability and AI in radiology: a double-edged sword. Radiology. 2024;310(2):e232030. doi:10.1148/radiol.232030
4.
HannemanKSzava-KovatsABurbridgeB, et al. Canadian Association of Radiologists statement on environmental sustainability in medical imaging. Can Assoc Radiol J. 2025;76(1):44-54. doi:10.1177/08465371241260013
5.
GonzalesAGuruswamyGSmithSR. Synthetic data in health care: a narrative review. PLOS Digit Health. 2023;2(1):e0000082. doi:10.1371/journal.pdig.0000082
6.
DooFXParekhVSKanhereA, et al. Evaluation of climate-aware metrics tools for radiology informatics and artificial intelligence: toward a potential radiology ecolabel. J Am Coll Radiol. 2024;21(2):239-247. doi:10.1016/j.jacr.2023.11.019
7.
KoetzierLRWuJMastrodicasaD, et al. Generating synthetic data for medical imaging. Radiology. 2024;312(3):e232471. doi:10.1148/radiol.232471
8.
DietrichNGongBPatlasMN. Adversarial artificial intelligence in radiology: attacks, defenses, and future considerations. Diagn Interv Imaging. Published online May 21, 2025. doi:10.1016/j.diii.2025.05.006
9.
RockallAGAllenBBrownMJ, et al. Sustainability in radiology: position paper and call to action from ACR, AOSR, ASR, CAR, CIR, ESR, ESRNM, ISR, IS3R, RANZCR, and RSNA. Can Assoc Radiol J. 2025;76(3):444-453. doi:10.1177/08465371251321390