From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and...

Area of Science:

• Anesthesiology and Perioperative Medicine
• Medical Artificial Intelligence
• Clinical Informatics

Background:

• Perioperative care is shifting from static decision-making to dynamic, adaptive systems.
• Clinical complexity necessitates advanced solutions for real-time patient management.
• Artificial intelligence (AI) offers a paradigm for integrating multimodal data and optimizing care.

Purpose of the Study:

• To review recent breakthroughs in AI for perioperative care, focusing on multimodal data fusion and closed-loop optimization.
• To analyze the impact of AI on anesthesia depth regulation and pain management.
• To identify challenges and propose a roadmap for AI integration in perioperative settings.

Main Methods:

• Systematic review of recent literature on AI in perioperative care.
• Analysis of multimodal data fusion architectures and closed-loop optimization techniques.
• Examination of AI applications in anesthesia depth and pain management.

Main Results:

• Contemporary perioperative environments require sophisticated AI for real-time data integration and dynamic response.
• Key implementation bottlenecks include computational optimization, robustness assurance, and interpretability.
• AI integration promises significant advancements in optimizing patient outcomes.

Conclusions:

• AI integration represents a significant transformation in perioperative care.
• Standardized datasets, human-AI collaboration, and ethical frameworks are crucial for future progress.
• Synergy between technological innovation and clinical expertise is vital for successful AI implementation.