Errors and discrepancies in radiology practice are uncomfortably common, with an estimated day-to-day rate of 3–5% of studies reported, and much higher rates reported in many targeted studies. Nonetheless, the meaning of the terms “error” and “discrepancy” and the relationship to medical negligence are frequently misunderstood. This review outlines the incidence of such events, the ways they can be categorized to aid understanding, and potential contributing factors, both human- and system-based. Possible strategies to minimise error are considered, along with the means of dealing with perceived underperformance when it is identified. The inevitability of imperfection is explained, while the importance of striving to minimise such imperfection is emphasised. Teaching Points • Discrepancies between radiology reports and subsequent patient outcomes are not inevitably errors. • Radiologist reporting performance cannot be perfect, and some errors are inevitable. • Error or discrepancy in radiology reporting does not equate negligence. • Radiologist errors occur for many reasons, both human- and system-derived. • Strategies exist to minimise error causes and to learn from errors made.

Artificial intelligence (AI) is poised to change much about the way we practice radiology in the near future. The power of AI tools has the potential to offer substantial benefit to patients. Conversely, there are dangers inherent in the deployment of AI in radiology, if this is done without regard to possible ethical risks. Some ethical issues are obvious; others are less easily discerned, and less easily avoided. This paper explains some of the ethical difficulties of which we are presently aware, and some of the measures we may take to protect against misuse of AI.

Background: The coronavirus disease 2019 (COVID-19) pandemic led to far-reaching restrictions of social and professional life, affecting societies all over the world. To contain the virus, medical schools had to restructure their curriculum by switching to online learning. However, only few medical schools had implemented such novel learning concepts. We aimed to evaluate students’ attitudes to online learning to provide a broad scientific basis to guide future development of medical education. Methods: Overall, 3286 medical students from 12 different countries participated in this cross-sectional, web-based study investigating various aspects of online learning in medical education. On a 7-point Likert scale, participants rated the online learning situation during the pandemic at their medical schools, technical and social aspects, and the current and future role of online learning in medical education. Results: The majority of medical schools managed the rapid switch to online learning (78%) and most students were satisfied with the quantity (67%) and quality (62%) of the courses. Online learning provided greater flexibility (84%) and led to unchanged or even higher attendance of courses (70%). Possible downsides included motivational problems (42%), insufficient possibilities for interaction with fellow students (67%) and thus the risk of social isolation (64%). The vast majority felt comfortable using the software solutions (80%). Most were convinced that medical education lags behind current capabilities regarding online learning (78%) and estimated the proportion of online learning before the pandemic at only 14%. In order to improve the current curriculum, they wish for a more balanced ratio with at least 40% of online teaching compared to on-site teaching. Conclusion: This study demonstrates the positive attitude of medical students towards online learning. Furthermore, it reveals a considerable discrepancy between what students demand and what the curriculum offers. Thus, the COVID-19 pandemic might be the long-awaited catalyst for a new “online era” in medical education.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.• Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.• AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.

Objectives The objective of these recommendations is to highlight the importance of infection prevention and control in ultrasound (US), including diagnostic and interventional settings. Methods Review of available publications and discussion within a multidisciplinary group consistent of radiologists and microbiologists, in consultation with European patient and industry representatives. Recommendations Good basic hygiene standards are essential. All US equipment must be approved prior to first use, including hand held devices. Any equipment in direct patient contact must be cleaned and disinfected prior to first use and after every examination. Regular deep cleaning of the entire US machine and environment should be undertaken. Faulty transducers should not be used. As outlined in presented flowcharts, low level disinfection is sufficient for standard US on intact skin. For all other minor and major interventional procedures as well as all endo-cavity US, high level disinfection is mandatory. Dedicated transducer covers must be used when transducers are in contact with mucous membranes or body fluids and sterile gel should be used inside and outside covers. Conclusions Good standards of basic hygiene and thorough decontamination of all US equipment as well as appropriate use of US gel and transducer covers are essential to keep patients safe. Main messages • Transducers must be cleaned/disinfected before first use and after every examination. • Low level disinfection is sufficient for standard US on intact skin. • High level disinfection is mandatory for endo-cavity US and all interventions. • Dedicated transducer covers must be used for endo-cavity US and all interventions. • Sterile gel should be used for all endo-cavity US and all interventions.