The American journal of medicine
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Randomized Controlled Trial
Machine Learning Predicting Atrial Fibrillation as an Adverse Event in the Warfarin Versus Aspirin in Reduced Cardiac Ejection Fraction (WARCEF) Trial.
Atrial fibrillation and heart failure commonly coexist due to shared pathophysiological mechanisms. Prompt identification of patients with heart failure at risk of developing atrial fibrillation would allow clinicians the opportunity to implement appropriate monitoring strategy and timely treatment, reducing the impact of atrial fibrillation on patients' health. ⋯ The use of machine learning can prove useful in identifying novel cardiac risk factors. Our analysis has shown that "social factors," such as living alone, may disproportionately increase the risk of atrial fibrillation in the under-represented non-White patient group with heart failure, highlighting the need for more studies focusing on stratification of multiracial cohorts to better uncover the heterogeneity of atrial fibrillation.
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In this study, we evaluated the diagnostic accuracy of Google Bard, a generative artificial intelligence (AI) platform. ⋯ While physicians excelled overall, and particularly with case reports, Google Bard displayed comparable diagnostic performance in common cases. This suggested that Google Bard possesses room for further improvement and refinement in its diagnostic capabilities. Generative AIs, including Google Bard, are anticipated to become increasingly beneficial in augmenting diagnostic accuracy.
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Morbidity and mortality related to heart failure are increasing and disparities are widening. These alarming trends, often confounded by access to care, are poorly understood. This study evaluates the prevalence of all stages of heart failure by race and socioeconomic status in an environment with no access barrier to care. ⋯ All stages of heart failure are highly prevalent among MHS beneficiaries of working age and, in an environment with no access barrier to care, there are striking disparities by race and socioeconomic status. The high prevalence of preclinical heart failure, particularly notable among Black beneficiaries, delineates a critical time window for prevention.