The effect of AI-assisted bedside echocardiography on inpatient care: a prospective trial.

Aims: Focused cardiac ultrasound (FoCUS) can yield valuable information for decision-making. However, it is limited by the skills required to acquire and interpret high-quality images. Machine learning algorithms can help mitigate this gap by providing guidance for optimal image acquisition and interpretation. We aimed to evaluate the impact of an artificial intelligence (AI) assisted, FDA-cleared FoCUS platform on clinical decision-making. Methods and results: This was a prospective trial with pre and post sequential allocation, conducted in two internal medicine departments. During the first 2 months, physicians with no formal echocardiography training used a common commercial FoCUS device as a complementary tool for their bedside patient evaluations. Then, during the following 4 months, an AI cloud-based platform was added, providing real-time feedback for image acquisition and AI-based echocardiographic results. The primary outcome was change in care following FoCUS, as reported by physicians after the examination and verified by assessors, which was analysed by generalized linear mixed model accounting for physician and department effects. Two hundred and eighty-one patients met the inclusion criteria and underwent FoCUS, 110 (39%) without AI assistance (control) and 171 with the AI. The most common reasons for FoCUS were worsening dyspnoea (50%) and chest pain (20%). A non-significant trend was observed in physician-reported new echocardiographic findings towards the AI group (43% vs.34%, Conclusion: AI-assisted FoCUS led to a higher rate of treatment plan changes, highlighting its potential to enhance bedside cardiac evaluation and optimize patient management.