OBJECTIVES: Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is heterogenous and limited by infrequent caseload or inadequate instructor feedback for satisfactory competencies. A new artificial intelligence (AI) navigational system using augmented reality – the Ambu Broncho Simulator – can guide bronchoscopy training. Is training with the AI system comparable to bedside, expert tutor instruction in improving bronchoscopy performance? DESIGN: A nonblinded, parallel group randomized controlled trial was conducted. SETTING: The study was conducted in a simulated setting at an academic university hospital. SUBJECTS: Critical-care physicians were invited to take part in the study. INTERVENTIONS: Forty participants received 30 minutes of bronchoscopy training, either guided by AI only (artificial intelligence group [AIG]) or by expert tutor feedback (expert tutor group [ETG]). All participants performed a final full navigation bronchoscopy performance test and completed a cognitive load questionnaire, the NASA Task Load Index . MEASUREMENTS AND MAIN RESULTS: Mean intersegmental time (MIT = PT/DC), diagnostic completeness (DC), procedure time (PT), structured progress (SP), and number of segments revisited (SR) were measured. The primary outcome measure assessed was MIT, a measure of bronchoscopic performance efficiency. The secondary outcome measures were DC, PT, SP, and SR. Nineteen participants were randomized to the AIG and 21 participants to the ETG. MIT, PT, and SR were significantly better in the AIG compared to the ETG (median difference, p): MIT (–7.9 s, 0.027), PT (–77 s, 0.022), SR (–7 segments, 0.019); all showing moderate effect sizes (0.35, 0.36, and 0.37, respectively) as per Cohen’s classification. There was no significant difference between the groups for all other final test measures. CONCLUSIONS: Training using an AI system resulted in faster and more efficient bronchoscopy performance by critical-care physicians when compared to expert human tutor instruction. This could change the future of bronchoscopy training in critical care and warrants validation in patients through clinical studies.

COVID-19-associated mucormycosis (CAM) emerged as an epidemic in certain parts of the world amidst the global COVID-19 pandemic. While rhino–orbital mucormycosis was well reported during the pandemic, in the absence of routine diagnostic facilities including lower airway sampling, pulmonary mucormycosis was probably under-recognized. In this review, we have focused on the epidemiology and management of COVID-19-associated pulmonary mucormycosis (CAPM). CAPM is a deadly disease and mortality can be as high as 80% in the absence of early clinical suspicion and treatment. While histopathological examination of tissue for angio-invasion and cultures have remained gold standard for diagnosis, there is an increasing interest in molecular and serological methods to facilitate diagnosis in critically ill patients and often, immune-suppressed hosts who cannot readily undergo invasive sampling. Combined medical and surgical treatment offers more promise than standalone medical therapy. Maintaining adequate glycemic control and prudent use of steroids which can be a double-edged sword in COVID-19 patients are the key preventative measures. We would like to emphasize the urgent need for the development and validation of reliable biomarkers and molecular diagnostics to facilitate early diagnosis.