Artificial Intelligence in Anaesthesiology: Current Applications, Challenges, and Future Directions

Area of Science:

• Anesthesiology
• Artificial Intelligence
• Perioperative Medicine

Background:

• Artificial intelligence (AI), encompassing machine learning, deep learning, and large language models, is rapidly advancing.
• AI integration is transforming various aspects of anesthesiology and perioperative care.

Purpose of the Study:

• To review current AI applications in anesthesiology.
• To identify methodological, ethical, and practical challenges hindering AI integration.
• To discuss future directions for safe and effective AI adoption in perioperative settings.

Main Methods:

• Comprehensive literature review of AI applications in anesthesiology.
• Analysis of AI's role in preoperative, intraoperative, and critical care settings.
• Evaluation of AI's impact on clinical decision-making, imaging, and education.

Main Results:

• AI tools are applied in risk stratification, monitoring, imaging interpretation, airway assessment, regional anesthesia, and critical care.
• Specific applications include automated ASA classification, complication prediction, depth-of-anesthesia estimation, and hemodynamic management.
• AI enhances echocardiography, computer vision for airway evaluation, and early detection of critical conditions like sepsis.

Conclusions:

• AI offers significant potential to improve perioperative care, clinical decision-making, and workflow efficiency.
• Challenges related to accuracy, ethical considerations, and practical integration must be addressed.
• Future research should focus on the safe and effective implementation of AI in anesthesiology.