AI in Cardiovascular Imaging: Solving Complexity with Intelligence

Artificial intelligence continues to gain ground across healthcare, but no specialty appears more ready, or more in need, of transformation than cardiovascular care. From improving diagnostics to enabling automation, AI in cardiovascular imaging is creating new possibilities for how providers detect, monitor, and intervene in heart disease. At LSI USA ’25, industry leaders from GE HealthCare, CorVista Health, Vista.ai, and Recode Ventures explored why cardiovascular medicine has become a proving ground for AI’s most advanced use cases, and what’s next.

Why Cardiology Leads the AI Curve

“Cardiology is rich with data: structured, multimodal data. Many problems in this field lend themselves well to automation and pattern recognition,” said Gera, who leads Cardiovascular and Interventional solutions at GE HealthCare. “It’s not just about workflow or clinical decision support. AI can elevate itself across the care continuum, from screening to diagnostics to intervention, all the way through follow-up.”

Vidian, Co-Founder and Co-Managing Partner at Recode Ventures, reinforced that trend from an investor’s lens. “We invest at the intersection of AI and healthcare because we believe AI is going to be fundamentally disruptive to CapEx and OpEx.”

But as Lam, CEO of CorVista Health, emphasized, cardiovascular care is not only data-rich but problem-rich. “It’s a multifactorial issue,” he said. “Access, compliance, adherence, and behavioral inertia all play a role. And because the problem is complex, the solution has to be multifactorial too. That’s where different types of AI for different use cases come in.”

Data, Complexity, and the Role of AI in Cardiovascular Imaging

The panelists agreed that imaging, especially cardiac MRI, represents one of the most immediate opportunities for AI-enabled improvement. “Cardiovascular imaging is very complicated,” said Hawkins, CEO of Vista.ai. “You have to manage breathing, EKGs, angles, and tissue properties. Properly trained AI, with the right datasets, can manage all that complexity better than a human.”

That complexity, Hawkins noted, is exactly why so few MRI machines in the US are used regularly for cardiac imaging: just 2 percent. “It’s the clinical gold standard, but it’s literally too hard to do. AI can change that.”

The quality of datasets is also a defining factor in whether a solution can move beyond point algorithms into system-level change. “We get excited when the data becomes multimodal,” Gera said. “That’s when you can start solving bigger problems and stitching together workflows across the care continuum.”

This blog is originally published here: https://www.lsiusasummit.com/news/ai-in-cardiovascular-imaging-solving-complexity-with-intelligence