Intelligent Cardiac Imaging Centered on the Patient

October 28nd, 2026 7:00~8:15 PM EDT | 4:00~5:15 PM PDT

This thought-leadership roundtable brings together leading experts to explore how advances in cardiac imaging and artificial intelligence are reshaping cardiovascular care while keeping the patient at the center of the journey. Through discussion of cardiac MRI in hypertrophic cardiomyopathy and coronary CT angiography in stable chest pain, the panel will examine how imaging can clarify diagnosis, characterize phenotype, evaluate fibrosis, support differential diagnosis, inform risk discussions, and translate findings into coordinated patient care.

The conversation will also address the patient experience of cardiac imaging, the evolving role of AI in clinical practice, and the importance of radiologist–cardiologist collaboration in delivering more connected, patient-centered care. Attendees will gain insights into how intelligent imaging can support better outcomes while ensuring that technology remains anchored to what matters most—the patient.

Upon completing this webinar, participants will be able to:

1. Describe the clinical evidence supporting the complementary role of cardiac MRI in hypertrophic cardiomyopathy and CCTA in stable chest pain as guideline-endorsed tools, distinguishing established evidence from AI-augmented emerging capabilities, and explaining how imaging findings become phenotype characterization, risk assessment, prevention, treatment planning, and a clear next step.
2. Explain how intelligent imaging may support more confident and reproducible interpretation, including HCM phenotype recognition, wall-thickness and ventricular measurement, fibrosis/LGE assessment, differential diagnosis, CCTA stenosis assessment, CAD-RADS classification, and plaque evaluation, while articulating validated limitations including algorithmic bias, sex inequity, incomplete outcome data, and the emerging status of automated diagnosis and risk prediction.
3. Recognize the patient experience of cardiac imaging — including scanning anxiety, claustrophobia, breath-hold burden, procedure complexity, risk-related anxiety, uncertainty about exercise and future planning, and result communication failures — and identify at least three practice changes that could improve patient engagement, scan completion, and willingness to return for longitudinal follow-up.
4. Apply a patient-centered communication framework for sharing AI-assisted imaging results, addressing patient fears about AI involvement, incidental findings, HCM phenotype and fibrosis, inherited and family implications, and newly identified cardiovascular risk, and articulating the core information elements patients need when AI is used in their cardiac care.
5. Analyze the structural case for radiologist-cardiologist collaboration in cardiac imaging — including how siloed reporting harms patients, how AI amplifies rather than resolves attribution gaps, and how multidisciplinary imaging models involving genetics, electrophysiology, HCM specialist centres, and procedural teams improve communication, clinical ownership, and the patient journey from referral to result.
6. Evaluate how AI, by automating the tasks clinicians do not want to do, can recover time for the conversations patients most need — drawing on the Rajpurkar framework for human-AI collaboration and current evidence on ambient AI, scan acceleration, and automated reporting — while understanding why recovered time does not automatically become patient-facing time without deliberate institutional intent.