ReviewJournal of anesthesia and translational medicine2025
From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization.
Review in Journal of anesthesia and translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.