Revolutionizing Anesthesia Practice with AI-Assisted Referral Management - TAHA, BAHA; ALSHARAF, Yusuf; Al-Ameer , Dr Meral (International Journal of Innovative Research in Medical Science, 2023)
The article explores the burgeoning role of Artificial Intelligence (AI) in healthcare, particularly in anesthesia and surgical practices, with the ultimate aim of enhancing patient outcomes. It underscores the necessity for refining AI algorithms while addressing legal and ethical concerns. AI's potential applications in healthcare are manifold, including predicting perioperative risks, detecting intraoperative events, and identifying postoperative complications for early intervention. The integration of AI in the operating room (OR) aims to augment human capabilities rather than replace healthcare professionals, thereby improving surgical safety and outcomes. Significantly, AI is posited to enhance efficiency and quality of care in healthcare settings. This includes automating referral management in anesthesia clinics, which face challenges like referral backlogs and labor-intensive processes. AI can streamline these processes, reduce paperwork, alleviate patient anxiety, and provide real-time feedback for more accurate and timely interventions. The article also highlights various AI applications in anesthesia, such as personalized anesthetic management, vital sign monitoring, and trend analysis in anesthesia practice. Additionally, the article delves into AI's transformative potential in pharmaceutical research, particularly in Central Nervous System (CNS) therapeutics. It mentions a study named "ADVENTURE" by the University Hospital, Strasbourg, focusing on using AI for classifying and analyzing adverse events in anesthesia. Furthermore, the use of AI in pediatric anesthesia for preoperative assessment, risk stratification, and managing various intraoperative challenges is discussed. AI's impact on reducing MRI scan times and enhancing ultrasound-guided regional anesthesia is also highlighted. The article concludes with a discussion on the implementation of AI in healthcare. It emphasizes the need for accurate, diverse data sets and robust governance for successful AI integration. The goal is to streamline clinic operations, improve patient care quality, and increase patient satisfaction while ensuring AI's role as an aid, not a substitute, in clinical judgment.