The integration of artificial intelligence into healthcare has garnered significant attention, particularly in the domain of continuous vital sign monitoring for in-hospital patients. Continuous monitoring systems, augmented by AI algorithms, promise to revolutionize patient care by enabling early detection of clinical deterioration, reducing the burden on healthcare staff, and improving patient outcomes [1]. These systems leverage AI to analyze large volumes of data in real-time, identifying subtle patterns and anomalies that might elude human observation. However, despite these promises, discrepancies often exist between the anticipated capabilities of AI-driven systems and their actual performance in clinical settings. This review critically examines these gaps, highlighting the evidence from current research and real-world applications.
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