AI-Enhanced ECG Boosts Early Detection of Low Ejection Fraction in Hospital

Published Date : 20/06/2025 

A recent study reveals that AI-assisted electrocardiogram (ECG) significantly enhances the early detection of low ejection fraction (EF ≤50%) in hospitalized patients, without increasing echocardiogram use. 

A recent study has demonstrated that AI-assisted electrocardiogram (ECG) can significantly improve the early detection of low ejection fraction (EF ≤50%) in hospitalized patients. This pragmatic randomized controlled trial, conducted at a single academic medical center in Taiwan, evaluated the impact of an AI-enabled ECG algorithm on the diagnostic precision of low ejection fraction in patients under non-cardiologist care.

The study involved 13,631 inpatients who were randomized into two groups: the intervention group (n = 6,840) and the control group (n = 6,791). The intervention group received AI-assisted ECG interpretations, while the control group received standard care. The AI model flagged patients as high or low risk based on ECG-derived probabilities for low EF.

The primary outcome of the study was the 30-day incidence of newly diagnosed low EF. The results were compelling: the intervention group saw a significant increase in diagnosis rates (1.5% vs. 1.1%, HR 1.50, 95% CI: 1.11–2.03; p = 0.023), particularly among AI-identified high-risk patients (13.0% vs. 8.9%, HR 1.55, 95% CI: 1.08–2.21). Notably, overall echocardiogram utilization did not increase (17.1% vs. 17.3%, HR 1.00), but the diagnostic yield improved significantly. Among high-risk intervention patients who underwent echocardiography, 34.2% were diagnosed with low EF, compared to 20.2% in the control group (p < 0.001).

Post-hoc analysis further revealed that there was an increase in cardiology consultation among high-risk intervention patients (29.3% vs. 23.5%; p = 0.027). This suggests that AI alerts prompted more targeted diagnostic actions, leading to improved diagnostic efficiency without escalating the imaging burden or mortality rates.

The AI algorithm's real-time integration into clinical workflows enabled more accurate risk stratification and selective use of downstream resources. This study highlights the potential of AI in enhancing diagnostic precision and improving patient outcomes, particularly in non-cardiologist settings. The findings underscore the importance of leveraging AI technology to optimize healthcare resources and deliver more effective and efficient care.

In summary, the study provides strong evidence that AI-assisted ECG can significantly enhance the early detection of low ejection fraction in hospitalized patients, without increasing the overall use of echocardiograms. This approach not only improves diagnostic accuracy but also ensures that healthcare resources are used more effectively, ultimately leading to better patient outcomes. 

Frequently Asked Questions (FAQS): 

Q: What is low ejection fraction (EF ≤50%)?

A: Low ejection fraction (EF ≤50%) refers to a condition where the heart's left ventricle pumps less than 50% of the blood it contains with each heartbeat. This can be an indicator of heart failure or other cardiac issues.

Q: How does AI-assisted ECG work?

A: AI-assisted ECG uses advanced algorithms to analyze electrocardiogram data and flag patients as high or low risk for low ejection fraction. This helps healthcare providers make more informed decisions about further diagnostic actions.

Q: What were the key findings of the study?

A: The study found that AI-assisted ECG significantly increased the diagnosis rates of low ejection fraction without increasing the overall use of echocardiograms. It also improved diagnostic yield and prompted more targeted cardiology consultations.

Q: Who conducted the study?

A: The study was conducted at a single academic medical center in Taiwan and involved 13,631 inpatients randomized into intervention and control groups.

Q: What are the potential benefits of using AI in healthcare?

A: The potential benefits of using AI in healthcare include improved diagnostic accuracy, more efficient use of healthcare resources, and better patient outcomes. AI can help healthcare providers make more informed decisions and deliver more personalized care. 

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