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

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.

This was a prospective controlled trial with 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.

281 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 dyspnea (50%) and chest pain (20%). Rates of self-reported new echocardiographic findings were similar between the groups (43% vs.34%, p=0.11). The FoCUS led to a change of care more often in the AI group (32% vs. 20%, p=0.033). In multivariate analysis, AI use was an independent predictor for a FoCUS-led change of care (adjusted OR 2.05, 95% CI 1.13-3.73), an effect that was consisted in subgroup analysis and mixed effect general linear models. AI also led to a lower rate of inpatient formal echocardiographic examinations.

AI-assisted FoCUS led to a higher rate of treatment plan changes, highlighting its potential to enhance bedside cardiac evaluation and optimize patient management.

Study design and timeline