Whether ultrasound (US) should be incorporated into a medical undergraduate curriculum remains a matter of debate within the medical education arena. There are clear potential benefits to its early introduction particularly with respect to the study of living anatomy and physiology in addition to the learning of clinical skills and procedures required for the graduate clinical practice. However, this needs to be balanced against what is perceived as an added value in addition to financial and time constraints which may potentially lead to the sacrifice of other aspects of the curriculum. Several medical schools have already reported their experiences of teaching US either as a standalone course or as a fully integrated vertical curriculum. This article describes and discusses the initial experience of a UK medical school that has taken the steps to develop its own pragmatic vertical US curriculum based on clinical endpoints with the intent of using US to enhance the learning experience of students and equipping them with the skills required for the safe practice as a junior doctor.
COVID-19 can cause damage to the lung, which can result in progressive respiratory failure and potential death. Chest radiography and CT are the imaging tools used to diagnose and monitor patients with COVID-19. Lung ultrasound (LUS) during COVID-19 is being used in some areas to aid decision-making and improve patient care. However, its increased use could help improve existing practice for patients with suspected COVID-19, or other lung disease. A limitation of LUS is that it requires practitioners with sufficient competence to ensure timely, safe, and diagnostic clinical/imaging assessments. This commentary discusses the role and governance of LUS during and beyond the COVID-19 pandemic, and how increased education and training in this discipline can be undertaken given the restrictions in imaging highly infectious patients. The use of simulation, although numerical methods or dedicated scan trainers, and machine learning algorithms could further improve the accuracy of LUS, whilst helping to reduce its learning curve for greater uptake in clinical practice.
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