Artificial intelligence has transformative potential across anesthesiology subspecialties, from perioperative management to operating room efficiency and education, though clinical validation and interoperability remain key barriers.
Abstract Modern anesthesiology has expanded beyond intraoperative care. It now integrates pain management, critical care, and emergency resuscitation. However, it still faces challenges like biological variability in drug responses, unpredictable intraoperative crises, and complex perioperative complications. Artificial intelligence (AI) emerges as a transformative force, can effectively enhance clinical quality and operational efficiency by extracting critical insights from vast amounts of healthcare data including electronic health records, vital sign waveforms, and imaging databases. AI applications in clinical anesthesia span the entire perioperative period, encompassing preoperative risk assessment, intraoperative physiological monitoring with adverse event prediction and visualized procedural guidance, as well as postoperative outcome forecasting and dynamic adaptive individualized treatment to enhance recovery after surgery. Beyond direct patient care, AI enhances operating room efficiency and revolutionizes anesthesia education. Despite progress, challenges persist in algorithm generalizability, data interoperability, and clinical validation. This review synthesizes the transformative role of AI across anesthesiology subspecialties, analyzes the barriers to implementation, and proposes strategic directions to bridge technological innovation with clinical optimization.
Shu et al. (Mon,) studied this question.