BackgroundPoint-of-care lung ultrasound (LUS) has been described for the evaluation of lung pathologies such as pneumothorax, pneumonia, and COVID-19 infections. It is rapidly deployed, portable, and accurate for LUS diagnoses. However, a learning curve limits its use, and teleguidance has been proposed as a solution. In this study, we primarily seek to measure the effect of tele-guided lung ultrasound (T-LUS) on chest X-ray (CXR) utilization in patients presenting with COVID-19 symptoms. Secondarily, we measure the effect of T-LUS on clinical decision-making, length of stay, and clinical outcomes.ResultsWe performed a retrospective observational study using a before–after design in an adult urgent care (AUC) setting. A total of 303 patients with symptoms suggestive of COVID-19 were included. AUC providers used T-LUS on 31% of patients with COVID-19 symptoms (n = 34). Abnormal LUS findings were found in 41% of patients (n = 14), with B-lines (86%) and pleural irregularities (79%) being the most common findings. Among all patients in the study period, those who received a T-LUS did not show a statistically significant difference in CXR utilization [−12% difference; 95% confidence interval (CI) −25% to 5%] as compared to patients who did not receive a T-LUS, and a similarly non-significant difference was observed in the intervention period (−5% difference; 95% CI: −21% to 14%). Length of stay was longer for patients in whom T-LUS was used (median difference 26 min, 95% CI 11–41). However, a comparison of patients in the intervention period revealed no significant difference in length of stay between patients who received T-LUS and those that did not (median difference 16 min, 95% CI −5 to 37).ConclusionT-LUS is feasible and alters clinical decision-making for novice ultrasound users in the care of patients with suspected COVID-19 infection. Our results indicated that there was a no statistically significant difference trend in CXR utilization and no improvement in length of stay by the end of the 2-week trial.
Epistemic and ethical limits of large language models in evidence-based medicine: from knowledge to judgment
BackgroundThe rapid evolution of general large language models (LLMs) provides a promising framework for integrating artificial intelligence into medical practice. While these models are capable



