Artificial intelligence (AI) is rapidly transforming cardiovascular care, from early detection and risk prediction to imaging analysis and personalized therapy. The AI in Cardiology session examines how machine learning, deep learning, and advanced analytics are being integrated into everyday clinical workflows. Participants will explore real examples of AI-assisted ECG interpretation, imaging quantification, automated risk scoring, and decision support systems that help clinicians optimize diagnosis, triage, and treatment. The focus is on practical, clinically relevant tools rather than abstract theory, enabling attendees to understand how AI can complement—not replace—expert clinical judgment.

Clinicians, researchers, and industry professionals increasingly look for an Cardiology conference to keep pace with the field’s rapid evolution. This session outlines how AI models are trained, validated, and deployed, with attention to data quality, bias, interpretability, and regulatory considerations. Attendees will see how AI is being used to flag silent left ventricular dysfunction, refine echocardiographic measurements, automate coronary calcium scoring, and predict clinical events such as heart failure admissions or arrhythmias before they manifest. Real-world projects and implementation stories will highlight both successes and challenges in bringing AI from research environments into routine patient care.

A central theme is the role of machine learning in cardiology for unlocking insights from large datasets—electronic health records, imaging archives, wearable device streams, and registries. Participants will learn how integrated data platforms can support population health management, personalized risk stratification, and targeted interventions. Case discussions will explore opportunities in preventive cardiology, virtual care pathways, and remote monitoring, showcasing how AI-driven tools can identify high-risk patients earlier, reduce diagnostic delays, and support efficient allocation of resources in busy cardiovascular centers.

The session also addresses ethical, legal, and professional implications of AI adoption. Topics include transparency in algorithms, patient consent, data privacy, and the need for ongoing human oversight. Attendees will discuss how to evaluate vendor solutions, participate in collaborative innovation, and build multidisciplinary teams that include clinicians, data scientists, and IT specialists. By the end of the session, participants will have a clearer roadmap for integrating AI into their own practices or institutions in a responsible, evidence-based way that benefits patients and care teams alike.