AI in Cardiovascular Care: Integrating the Future Into Clinical Practice Today-Slides: Dr. Elias

Slides: Dr. Elias

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Dr. Pierre Elias, Assistant Professor at Columbia University and Medical Director for AI at NewYork-Presbyterian, highlights the transformative potential of AI in cardiology. Traditional human interpretation of medical imaging and clinical notes is impressive but imperfect, leaving vast complexities and data underutilized. Deep learning and AI can analyze high-dimensional data (such as CT scans, ECGs, and entire electronic health records) far beyond traditional statistical methods, enabling discovery, diagnosis, and impactful clinical decision-making.<br /><br />Current AI models are now matching or exceeding human expert performance in specific diagnostic tasks. For example, AI applied to ECGs can accurately detect individual and composite cardiac diseases, validated across multiple hospital systems and even wearable devices. A notable challenge is identifying which patients with ECGs but no echocardiogram (echo) actually require one—AI models are helping to optimize this decision, addressing the problem that half of high-risk patients do not get necessary echos.<br /><br />AI also improves echocardiogram interpretation through deep learning techniques, providing more precise phenotyping of cardiac function. Ambient AI tools, which assist clinicians by automating documentation and reducing cognitive load, show some benefits though efficiency gains remain uncertain and adoption challenges persist, as reflected in mixed clinician satisfaction.<br /><br />Dr. Elias proposes a practical Five Question Framework for AI integration: identifying underutilized high-dimensional data, sufficient case volume, availability of reliable labels (gold standards), clinical relevance of predicted effect sizes, and trust in AI outputs. These considerations guide whether AI can effectively enhance clinical decision-making.<br /><br />Ongoing work, including a two-part cardiovascular AI review forthcoming in JACC, promises further insights. With AI reimagining workflows and enabling superior diagnostics, Dr. Elias envisions an AI-driven future in cardiology that augments and surpasses traditional expert capabilities.

Keywords

Artificial Intelligence

Cardiology

Deep Learning

Medical Imaging

ECG Analysis

Echocardiogram

Clinical Decision-Making

AI Integration Framework

Wearable Devices

AI in Healthcare