Intensive care unit (ICU) patients often experience rapid changes in clinical status, requiring timely identification of deterioration to guide life-sustaining interventions. Current artificial intelligence (AI) models for acuity assessment rely on m...
Anesthesiology and critical care medicine contain a vast repository of patient data that can be analyzed and decoded by artificial intelligence applications. Although there has been a rapid growth in the development of predictive analytics to improve...
Artificial Intelligence (AI) is rapidly transforming the landscape of critical care, offering opportunities for enhanced diagnostic precision and personalized patient management. However, its integration into ICU clinical practice presents significan...
BACKGROUND: Language barriers pose a significant barrier to expanding access to critical care education worldwide. Machine translation (MT) offers significant promise to increase accessibility to critical care content, and has rapidly evolved using n...
Artificial intelligence (AI) has the potential to revolutionize anesthesiology, pain medicine, and intensive care by enhancing decision-making, minimizing errors, and personalizing patient care. However, the practical and ethical challenges it presen...
The article explores the transformative impact of artificial intelligence (AI) in critical care medicine. AI models offer superior accuracy in mortality risk assessment and personalized treatment recommendations, enhancing patient outcomes in acute a...
BACKGROUND: The Medical Information Mart for Intensive Care (MIMIC) database has become a cornerstone resource for critical care research, enabling advances in outcome prediction, machine learning, and patient management. However, comprehensive bibli...
PURPOSE OF REVIEW: Machine perfusion has emerged as a transformative alternative to static cold storage in liver transplantation, necessitating a comprehensive review of current evidence. This article examines recent advances in preservation techniqu...
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...
BACKGROUND: Classification trees (CTs) are widely used machine learning algorithms with growing applications in clinical research, especially for risk stratification. Their ability to generate interpretable decision rules makes them attractive to hea...
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