Integrative Artificial Intelligence in Regional Anesthesia: Enhancing Precision, Efficiency, Outcomes and Limitations

Abstract

Artificial intelligence (AI) has made remarkable progress in various domains, outperforming human capabilities in many areas. It is no surprise that AI is being increasingly used in healthcare practices, including regional anesthesia. Recent advancements in AI have enabled its integration into the field of regional anesthesia, promising to enhance precision, efficiency, and patient outcomes. By utilizing machine learning algorithms and predictive analytics, AI has the potential to revolutionize the way regional anesthesia procedures are conducted and managed. Ultrasound-guided regional anesthesia (UGRA) significantly enhances the success rates of regional blocks while mitigating complication risks. This review scrutinizes the burgeoning role of artificial intelligence (AI) in UGRA, detailing its evolution and pivotal function in optimizing sonographic imaging, target delineation, needle guidance, and local anesthetic administration. AI's support is invaluable, particularly for non-experts in training and clinical practice and for experts in educational settings. By systematically analyzing the capabilities and applications of AI in regional anesthesia, we assess its contribution to procedural precision, safety, and educational advancement. The findings reveal that AI-assisted UGRA not only bolsters the accuracy of anatomical identification, thus improving patient safety, but also standardizes the quality of care across varying expertise levels. The integration of AI into UGRA emerges as a transformative influence in anesthesiology, promising to reshape the domain with enhanced precision, efficiency, and patient-centered care.