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

作者：Mingdi Xue, Jiaming Yang, Honglin Wang, Zineng Yan, Xiaoliang Chen, Weihang Gao, Rui Luo, Xiaodan Lv, Zhewei Ye · 发表于：Journal of Anesthesia and Translational Medicine · 年份：2025 · DOI：10.1016/j.jatmed.2025.06.003 · 被引用次数：6 · 研究领域：Artificial Intelligence in Healthcare and Education、Surgical Simulation and Training、Medical Imaging and Analysis

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 outc...