To explore the incorporation of artificial intelligence (AI) into echocardiography and the challenges of broader adoption.
Approach:
Building Better AI: Minh B. Nguyen discussed a shift from traditional supervised learning to self-supervised learning, allowing AI to learn from unlabeled echocardiograms.
Current Evidence: Sreekanth Vemulapalli examined validated and emerging AI use cases in echocardiography and outlined a roadmap for implementation.
AI Conundrums: Jose Donato A. Magno analyzed the potential benefits and limitations of AI in clinical echocardiography across ten domains.
Putting AI Into Practice: Karen Zimmerman illustrated real-world AI use in echocardiography, questioning its impact on patient management.
Key Findings:
Self-supervised learning can enhance AI robustness by teaching models the grammar of the heart.
Current evidence for AI in echocardiography is largely retrospective, with prospective studies needed.
Equitable access is a significant barrier to AI adoption in echocardiography.
Interpretation:
AI in echocardiography presents both opportunities and challenges that require careful consideration.
Limitations:
Much of the current evidence supporting AI is retrospective.
Equitable access to AI technology remains a critical issue.
Conclusion:
AI's successful integration into echocardiography depends on addressing its limitations.
An ensemble electrocardiogram model classified derived diastolic dysfunction risk phenotypes and stratified heart failure–related death risk across external cohorts, according to findings presented at ASE 2026.
