Artificial Intelligence in Anesthesia: Enhancing Precision, Safety, and Global Access Through Data-Driven Systems.

Area of Science:

• Anesthesiology
• Medical Informatics
• Artificial Intelligence

Background:

• Anesthesiology faces challenges in precision, safety, and responsiveness.
• Manual anesthetic management can be inconsistent and less responsive.
• AI offers adaptive systems to enhance clinical practice.

Purpose of the Study:

• To review the integration of Artificial Intelligence (AI) in anesthesiology.
• To focus on closed-loop systems and their autonomous control capabilities.
• To discuss the benefits, challenges, and future of AI in anesthesia.

Main Methods:

• Review of AI integration in anesthetic practice, focusing on closed-loop systems.
• Analysis of continuous physiologic inputs (BIS, EEG, heart rate, blood pressure) for real-time titration.
• Examination of predictive algorithms and AI applications in critical care and global health.

Main Results:

• AI-guided closed-loop systems provide more consistent and responsive anesthetic management.
• Predictive algorithms reduce intraoperative hypotension by up to 40%.
• AI systems improve anesthetic depth maintenance, shorten recovery times, and support critical care sedation.

Conclusions:

• AI enhances anesthesiology by improving precision, safety, and responsiveness.
• AI-guided systems augment clinician capacity, enabling personalized anesthesia delivery.
• Addressing ethical, legal, and access challenges is crucial for widespread AI adoption in anesthesia.