<![CDATA[This review article discusses how Artificial Intelligence for IT Operations (AIOPs) can transform healthcare monitoring by providing healthcare professionals with real-time data insights, predictive analytics, and intelligent automation. AIOPs can be used for predictive maintenance, anomaly detection, clinical decision support, and remote patient monitoring. The implementation of AIOPs in healthcare monitoring involves data collection, data processing, real-time monitoring, predictive maintenance, clinical decision support, and remote patient monitoring. Data collection involves the use of various sources of data such as electronic health records, medical devices, and patient monitoring systems. The data collected is processed using machine learning algorithms and big data analytics to provide insights into patient health. Real-time monitoring of patient vital signs allows healthcare professionals to monitor patients remotely, reducing the need for in-person visits. AIOPs can provide clinical decision support to healthcare professionals by analyzing patient data and suggesting treatment options based on best practices and clinical guidelines. Remote patient monitoring can track vital signs, medication adherence, and other health metrics, reducing healthcare costs, improving patient outcomes, and enhancing patient experience. However, the implementation of AIOPs in healthcare monitoring also poses challenges such as data privacy and security that need to be addressed to ensure effective and ethical use of AIOPs in healthcare.]]>
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