The Use of Artificial Intelligence in Medical Imaging: A Nationwide Pilot Survey of Trainees in Saudi Arabia

Artificial intelligence is dramatically transforming medical imaging. In Saudi Arabia, there are a lack of studies assessing the level of artificial intelligence use and reliably determining the perceived impact of artificial intelligence on the radiology workflow and the profession. We assessed the levels of artificial intelligence use among radiology trainees and correlated the perceived impact of artificial intelligence on the workflow and profession with the behavioral intention to use artificial intelligence. This cross-sectional study enrolled radiology trainees from Saudi Arabia, and a 5-part-structured questionnaire was disseminated. The items concerning the perceived impact of artificial intelligence on the radiology workflow conformed to the six-step standard workflow in radiology, which includes ordering and scheduling, protocoling and acquisition, image interpretation, reporting, communication, and billing. We included 98 participants. Few used artificial intelligence in routine practice (7%). The perceived impact of artificial intelligence on the radiology workflow was at a considerable level in all radiology workflow steps (range, 3.64–3.97 out of 5). Behavioral intention to use artificial intelligence was linearly correlated with the perceptions of its impact on the radiology workflow and on the profession (p < 0.001). Artificial intelligence is used at a low level in radiology. The perceived impact of artificial intelligence on radiology workflow and the profession is correlated to an increase in behavioral intention to use artificial intelligence. Thus, increasing awareness about the positive impact of artificial intelligence can improve its adoption

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population