The authors of this narrative review aimed to introduce quality metrics for emerging artificial intelligence (AI) papers, such as the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Field-Weighted Citation Impact (FWCI). Furthermore, the study dives into some of the top AI models for segmentation, detection, and classification, while concluding that prospective studies with multi-center design will need to determine the impact of AI on radiologists’ performance and the clinical management of prostate cancer. Key points Artificial intelligence (AI) offers potential applications for various steps of prostate magnetic resonance imaging workflow. Prostate segmentation, intraprostatic lesion detection, and classification AI tools are commonly reported in the literature with promising results. Prospective multicenter studies are needed to determine impact of AI on improving radiologist performance. Article: Tasks for artificial intelligence in prostate MRI Authors: Mason J. Belue & Baris Turkbey

Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a

