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Congratulations to the following Yale Department of Internal Medicine faculty members, who were recently promoted, appointed, or reappointed.
Yale investigators have identified a new artificial intelligence (AI)-based video biomarker that is able to identify those who might develop and have rapidly worsening aortic stenosis.
Yale faculty and trainees will present clinical research at the upcoming American College of Cardiology Scientific Meeting.
Rohan Khera, MD, assistant professor of medicine (cardiovascular medicine), discusses who may need to eat more salt.
Meet Evangelos Oikonomou, MD, DPhil, a clinical fellow in the Section of Cardiovascular Medicine, who recently received a 2024 American Society for Clinical Investigation Emerging-Generation Award.
In an editorial, Rohan Khera, MD, MS, assistant professor of medicine (cardiovascular medicine), recommends a careful approach to evaluating AI tools before adopting them.
Rohan Khera, MD, assistant professor of medicine (cardiovascular medicine), weighs in on the importance of including Black women and other groups in clinical trials.
Artificial intelligence can help physicians spot heart disorders not seen by the human eye.
Entrepreneurial faculty and students at Yale School of Public Health have a partner in Yale Ventures.
The medication has the potential to markedly reduce the risk of heart attacks and other heart-related conditions among millions of Americans with obesity who have also been diagnosed with cardiovascular disease, a Yale study shows.
View the most cited, read, and discussed research published over the summer.
Evangelos Oikonomou received an F32 NIH fellowship to design screening programs for hypertrophic cardiomyopathy, an under-diagnosed heart condition.
A novel application of deep learning could detect severe aortic stenosis early, researchers say.
Three investigators in training from the Yale Cardiovascular Data Science (CarDS) Lab were invited to present their research results along with other finalists at the 2023 American Heart Association (AHA) Scientific Sessions.
Artificial intelligence can read an electrocardiogram to find structural heart problems.
Those with the highest risk of heart disease are less likely to use apps and wearables that aim to help improve health.
Two in five U.S. adults with or at risk for heart disease use smartphones or tablets to monitor their health, a new Yale study finds
Wearable devices often pick up noisy electrocardiograms (ECGs), which can hinder artificial intelligence (AI)-based detection of cardiovascular disease. In a new paper published in npj Digital Medicine, researchers from Yale Cardiovascular Medicine and Computer Science developed a noise-adapted AI model that can detect left ventricular systolic dysfunction (LVSD) from ECGs obtained by wearable devices.
The model also is able to predict which patients who don’t yet have an LVEF < 40% will go on to develop systolic dysfunction.
A breakthrough in cardiac diagnostics: an AI-powered deep learning application now enables automated screening for left ventricular (LV) systolic dysfunction.