he prospect of improved clinical outcomes and more efficient health systems has fueled a rapid rise in the development and evaluation of AI systems over the last decade. Because most AI systems within healthcare are complex interventions designed as clinical decision support systems, rather than autonomous agents, the interactions among the AI systems, their users and the implementation environments are defining components of the AI interventions' overall potential effectiveness. Therefore, bringing AI systems from mathematical performance to clinical utility needs an adapted, stepwise implementation and evaluation pathway, addressing the complexity of this collaboration between two independent forms of intelligence, beyond measures of effectiveness alone 1 . Despite indications that some AI-based algorithms now match the accuracy of human experts within preclinical in silico studies 2 , there

Objectives To explore radiologists’ opinions regarding the shift from in-person oncologic multidisciplinary team meetings (MDTMs) to online MDTMs. To assess the perceived impact of online MDTMs, and to evaluate clinical and technical aspects of online meetings. Methods An online questionnaire including 24 questions was e-mailed to all European Society of Oncologic Imaging (ESOI) members. Questions targeted the structure and efficacy of online MDTMs, including benefits and limitations. Results A total of 204 radiologists responded to the survey. Responses were evaluated using descriptive statistical analysis. The majority (157/204; 77%) reported a shift to online MDTMs at the start of the pandemic. For the most part, this transition had a positive effect on maintaining and improving attendance. The majority of participants reported that online MDTMs provide the same clinical standard as in-person meetings, and that interdisciplinary discussion and review of imaging data were not hindered. Seventy three of 204 (35.8%) participants favour reverting to in-person MDTs, once safe to do so, while 7/204 (3.4%) prefer a continuation of online MDTMs. The majority (124/204, 60.8%) prefer a combination of physical and online MDTMs. Conclusions Online MDTMs are a viable alternative to in-person meetings enabling continued timely high-quality provision of care with maintained coordination between specialties. They were accepted by the majority of surveyed radiologists who also favoured their continuation after the pandemic, preferably in combination with in-person meetings. An awareness of communication issues particular to online meetings is important. Training, improved software, and availability of support are essential to overcome technical and IT difficulties reported by participants. Key Points • Majority of surveyed radiologists reported shift from in-person to online oncologic MDT meetings during the COVID-19 pandemic. • The shift to online MDTMs was feasible and generally accepted by the radiologists surveyed with the majority reporting that online MDTMs provide the same clinical standard as in-person meetings. • Most would favour the return to in-person MDTMs but would also accept the continued use of online MDTMs following the end of the current pandemic.

Colorectal cancer (CRC) represents one of the most relevant causes of morbidity and mortality in Western societies. CRC screening is actually based on faecal occult blood testing, and optical colonoscopy still remains the gold standard screening test for cancer detection. However, computed tomography colonography (CT colonography) constitutes a reliable, minimally-invasive method to rapidly and effectively evaluate the entire colon for clinically relevant lesions. Furthermore, even if the benefits of its employment in CRC mass screening have not fully established yet, CT colonography may represent a reasonable alternative screening test in patients who cannot undergo or refuse colonoscopy. Therefore, the purpose of our review is to illustrate the most updated recommendations on methodology and the current clinical indications of CT colonography, according to the data of the existing relevant literature.

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Meteosat Third Generation (MTG) is the next generation of European meteorological geostationary satellites, set to be launched in 2021. Besides ensuring continuity with Meteosat Second Generation imagery mission, the new series will feature new instruments, such as the Lightning Imager (LI), a high-speed optical detector providing near real-time lightning detection capabilities over Europe and Africa. The instrument will register events on pixels, where a lightning pulse generates a transient in the acquired radiance. In parallel, signal variations due to a number of unwanted sources, e.g., acquisition noise or jitter movement, are expected to produce false events. The challenge for on-board and on-ground processing is, thus, to discard as many false events as possible while keeping a majority of the true lightning events. This paper discusses a chain of algorithms that can be used by the LI for the detection of lightning and for the filtering of false events. Some of these algorithms have been developed in the framework of internal research and simulations conducted by the MTG team at the European Space Agency on an in-house LI simulator and therefore will not necessarily reflect the ultimate operational processing chain. The application of the chain on a representative scenario shows that 99.5% of the false events can be eliminated while keeping 83.6% of the true events, before generating the LI higher level data products. Machine learning techniques have also been studied as an alternative for on-ground event processing, and preliminary results indicate promising potential.