In this study, the authors aimed to develop and validate a patient questionnaire on the patients’ view on the implementation of artificial intelligence (AI) in radiology. Six domains from a previous study were used to develop the patient questionnaire. The questionnaire explored accountability, distrust, personal interaction, and efficiency, among other areas. Key points Although AI systems are increasingly developed, not much is known about patients’ views on AI in radiology. Since it is important that newly developed questionnaires are adequately tested and validated, we did so for a questionnaire measuring patients’ views on AI in radiology, revealing five factors. Successful implementation of AI in radiology requires assessment of social factors such as subjective norms towards the technology. Article: Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire Authors: Yfke P. Ongena, Marieke Haan, Derya Yakar & Thomas C. Kwee

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

