Renowned cardiovascular investigator, Dr. Rob Beanlands, delivers a thought-provoking lecture at ASNC2023, urging cardiac imagers to address research voids. He emphasizes the need for robust evidence to guide clinical decisions in heart patient care and highlights the challenges in conducting imaging trials that bridge the gap between research and real-world practice.
Read MoreThe cardiovascular diagnostic and monitoring devices market is experiencing significant growth, fueled by advancements in remote monitoring systems, AI integration, and the demand for portable devices.
Read MoreA study reveals the potential of AI-based quantitative coronary angiography (AI-QCA) in enhancing clinical decision-making. By accurately analyzing coronary stenotic lesions, AI-QCA offers real-time insights and guides physicians in determining optimal stent sizes.
Read MoreThe Enigma of Diagnosis Heart failure with reduced ejection fraction (HFrEF) presents a complex diagnostic challenge. The symptoms that bring a patient to the clinic are varied and often point to multiple possible diagnoses....
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Mount Sinai researchers have developed HeartBEiT, an AI model that interprets electrocardiograms (ECGs) as language, revolutionizing ECG analysis. With superior performance and “explainability,” HeartBEiT enhances diagnostic accuracy for cardiac conditions and offers insights into heart health. Discover the transformative potential of this groundbreaking innovation in the field of medical technology.
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