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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. u201cCardiovascular imaging is very complicated,u201d said Hawkins, CEO of Vista.ai. u201cYou 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.u201d For more information visit here: https://www.lsiusasummit.com/news/ai-in-cardiovascular-imaging-solving-complexity-with-intelligence

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

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  1. 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

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