Real-Time Diagnostics in Critical Care: AI for Rapid Decision-Making and Continuous Monitoring
DOI:
https://doi.org/10.47941/ijce.2658Keywords:
Artificial Intelligence (AI), Real-Time Diagnostics, Critical Care, ICU Monitoring, Predictive Analytics, Clinical Decision Support.Abstract
Purpose: The research examines artificial intelligence technology's (AI) ability to provide real-time medical diagnostics and decision-making solutions for critical care environments. The study targets high-acuity settings such as ICUs and emergency departments to analyze AI's capability to enhance clinical response times and decrease diagnostic delays while improving outcomes for sepsis multi-organ failure and acute respiratory events.
Methodology: A systematic literature review utilized PICO-based search terms, which examined PubMed alongside IEEE Xplore and JAMA AI databases. The search query utilized Boolean operators to retrieve results about "real-time AI" combined with "critical care diagnostics" and "emergency care AI" along with "point-of-care AI tools". Peer-reviewed studies published between 2021 and 2024 received priority for evaluation because they assessed AI-based models for real-time monitoring, predictive analytics, and edge AI deployments in critical care settings. The research focused on studies implementing reproducible validation methods using authentic clinical data sets.
Findings: Implementing AI models produced significant enhancements in early warning systems and real-time physiological monitoring and emergency diagnostics, surpassing conventional tools in terms of sensitivity and speed of inference. The deployment of edge AI systems in real-time allowed continuous vital sign data integration with lab and imaging inputs, which improved clinical decision-making through latency reduction. The integration of explainable AI frameworks (e.g., SHAP and LIME) within clinical workflows resulted in a 20% enhancement in diagnostic precision and a significant decrease in incorrect alerts according to study-based quantitative benchmarks.
A unique contribution to theory, practice, and policy (recommendations): The research builds theoretical knowledge about AI-based temporal modeling in changing clinical environments while demonstrating the practical advantages of implementing real-time AI directly into bedside medical equipment. The research supports a transformation from reactive to anticipatory healthcare practices enabled by AI-based early interventions. The study suggests that regulatory frameworks should be established to guarantee the ethical implementation of AI tools alongside strict clinical validation and system interoperability in critical care settings. The research presents an operational plan that stakeholders can utilize to build reliable, time-sensitive AI systems for medical frontlines.
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