Artificial Intelligence in Anesthesia, Critical Care, and Beyond: Current Applications, Future Prospects, and Limitations

Author(s): Michiaki Yamakage, Soshi Iwasaki, Atsushi Sawada, Tomohiro Chaki, Ken-Ichiro Kikuchi,Yusuke Iwamoto

The Artificial intelligence (AI) represents a paradigm shift in healthcare delivery, particularly in data-intensive, time-sensitive clinical specialties. Anesthesiology, intensive care medicine, and emergency medicine stand at the forefront of this technological transformation due to their reliance on continuous physiological monitoring, rapid decision-making, and complex data interpretation. Beyond acute care specialties, emerging applications extend to palliative care, pain management, and traditional East Asian medicine, demonstrating AI's versatility across diverse clinical contexts. This comprehensive narrative review synthesizes recent literature on AI applications across these specialties, providing a balanced assessment of current achievements, persistent limitations, and future research priorities. AI has demonstrated significant promise across multiple clinical domains, with applications including real-time anesthetic depth monitoring, predictive models for postoperative complications, ICU early warning systems, sepsis prediction algorithms, emergency department triage optimization, palliative care referral support, personalized pain management, and modernization of traditional medicine practices. Despite these advances, significant limitations persist, including lack of prospective validation in diverse populations, challenges in model interpretability, heterogeneity in data quality, and ongoing concerns regarding algorithmic bias and ethical implications. AI technology is positioned to augment rather than replace clinical expertise, offering enhanced precision, efficiency, and personalized patient care. However, successful implementation requires addressing fundamental challenges in model validation, regulatory approval, and clinical integration. Future progress depends critically on the development of explainable AI models, robust external validation, establishment of comprehensive regulatory frameworks, and thoughtful integration strategies that preserve essential human judgment, empathy, and the therapeutic relationship.