Objective:

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.

Sources:

• ASE 2026 Scientific Sessions