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Researchers developed AI models to better identify atrial fibrillation patients who need blood thinners, improving treatment.
Researchers from UCSF and Mount Sinai have developed AI models that help identify atrial fibrillation patients who need blood thinners to prevent strokes.
These models analyze patients' medical data to determine the risk of stroke versus bleeding, potentially reducing unnecessary treatments and improving patient outcomes.
The studies suggest a shift towards more personalized treatment plans for atrial fibrillation.
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Los investigadores desarrollaron modelos de IA para identificar mejor a los pacientes de fibrilación auricular que necesitan anticoagulantes, mejorando el tratamiento.