The relative effectiveness of AI-based imaging and ultrasound in the navigation of cardiac procedures

Abstract

The exponential growth in cardiovascular imaging investigations underlines a vital need to optimise clinical workflow efficiency and diagnostic accuracy. Artificial intelligence (AI), particularly machine learning, has emerged as a transformational technology in this sector, giving the potential to expedite cardiac imaging operations and enhance patient outcomes. This review investigates how well AI-based imaging navigates cardiac operations in comparison to traditional ultrasonography. We investigate AI's ability to automate picture segmentation, minimise operator-dependent variability, and combine multimodal data, such as cardiac magnetic resonance imaging, computed tomography, nuclear imaging, and echocardiography, for a whole cardiac evaluation. AI-enhanced imaging provides more accuracy in illness identification, prognosis, and clinical decision-making, even though conventional ultrasound is still a vital component of real-time procedure guidance. The article also identifies the main obstacles to the widespread use of AI in clinical practice, as well as current applications and developing technology. The study offers a critical viewpoint on the developing role of AI in improving cardiovascular care by contrasting different modalities.

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