From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization

Abstract

The integration of artificial intelligence (AI) into perioperative care represents a paradigmatic transformation from static decision frameworks to dynamic, adaptive systems. This comprehensive review synthesizes recent breakthroughs in multimodal data fusion and closed-loop optimization for perioperative care, examining the convergence of technological innovation with clinical imperatives. We systematically analyze three critical dimensions: the fundamental paradigm shift driven by clinical complexity, technological breakthroughs in multimodal fusion architectures, and dynamic decision-making implementations in anesthesia depth regulation and pain management optimization. Our analysis reveals that contemporary perioperative environments demand sophisticated AI systems capable of real-time data integration and dynamic response optimization. We identify critical bottlenecks in clinical implementation, including computational optimization under real-time constraints, robustness assurance mechanisms, and interpretability challenges. Looking forward, we delineate three interconnected pillars essential for advancing the field: standardized multimodal benchmark datasets, human-AI collaborative decision-making paradigms, and ethical-regulatory frameworks. This review establishes a comprehensive roadmap for transforming perioperative care through AI integration, emphasizing the synergistic relationship between technological innovation and clinical expertise in optimizing patient outcomes.

Ähnliche Arbeiten