Artificial Intelligence and Early Detection of Diabetic-Hypertensive Emergencies in the Emergency Room
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Abstract
The global surge in diabetes mellitus and hypertension has led to an increased incidence of acute metabolic and cardiovascular emergencies. Early recognition and timely management of these emergencies—such as diabetic ketoacidosis (DKA), hyperosmolar hyperglycemic state (HHS), hypertensive crisis, and stroke—are critical to prevent morbidity and mortality. Artificial Intelligence (AI) has emerged as a transformative tool in healthcare, capable of integrating clinical, biochemical, and hemodynamic data for real-time risk prediction and decision support. This narrative review explores the evolving role of AI in the early detection and management of diabetic-hypertensive emergencies in the emergency room (ER), highlighting pathophysiological interlinks, current applications, challenges, and future directions.
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