Artificial intelligence in onco-anaesthesia: Current applications, challenges, and future directions

Artificial intelligence (AI) is transforming onco-anaesthesia by shifting practice from reactive physiological management toward predictive and precision-based care. This review outlines current AI applications across the perioperative cancer pathway. Preoperatively, machine learning and deep learning models enhance risk stratification through automated frailty assessment, electronic health record phenotyping, and prediction of cancer-specific outcomes. Intraoperatively, AI-enabled technologies such as closed-loop anaesthesia delivery systems, predictive haemodynamic monitoring, and automated depth-of-anaesthesia control optimize drug dosing, reduce physiological stress, and may help preserve perioperative immune function, with potential implications for long-term oncologic outcomes. Postoperatively, AI-driven integration of multimodal data-including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms-facilitates early detection of complications such as delirium, persistent pain, acute kidney injury, and anastomotic leakage. The review also examines the role of AI in evaluating the "onco-anaesthesia hypothesis" by clarifying links between anaesthetic techniques, inflammation, and cancer recurrence. Despite these advances, significant challenges persist, including data heterogeneity, limited generalisability, algorithmic opacity, regulatory uncertainty, and ethical concerns related to equity and clinical implementation. Future progress will depend on explainable AI, federated learning, real-time clinical decision-support systems, and validation through large, prospective studies to fully realise AI's potential in personalised onco-anaesthetic care.