Recent approaches of artificial intelligence in intensive care unit: A review

Abstract

Complex environment of the intensive care unit (ICU), prompt and precise decision-making is essential to patient survival. Healthcare providers face issue including information overload, delayed decision making, and human error as result of increasing volume of different patient data. Because they enable effective data analysis, pattern identification and predictive modelling, recent advancement in artificial intelligence (AI) provides encouraging answers. This alters the delivery of critical care. The evolving use of AI in ICUs is examined in this paper. It discusses its application, advantages, disadvantages and technological foundation. Along with their common uses in critical care, it also examines AI techniques including machine learning (ML), deep learning (DL), natural language process (NLP), and expert system, predictive analytics, early sepsis detection, clinical decision support system, automated monitoring and insight from documentation powered by natural language processing are a just of few of the practical application of AI. The benefits of automation and robotics for improving productivity and patient care are also covered, including AI-based medication delivery system and robotics helper. However, there are several obstacles to applying AI in critical care units including a lack of consensus, algorithm bias, comprehending model decisions, and diverse data, personalized AI-driven care in ICU, edge computing and internet of medical things (IoMT) integration, and reinforcement learning for adaptive patient management are some future prospects. This image was created by using Biorender.com • Artificial intelligence supports timely clinical decisions and improves outcomes in intensive care. • Machine and deep learning models enable early detection of sepsis, AKI, and respiratory distress. • AI-based decision support enhances diagnostic accuracy and optimizes ICU resource utilization. • Advanced monitoring and alarm reduction systems strengthen patient safety in critical care. • Future prospects include explainable AI, IoMT integration, and robotic automation in ICUs.

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