Public articles linked to the same research event.
World Journal of Methodology This review outlines current applications of artificial intelligence across the perioperative cancer pathway in onco-anaesthesia: 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; postoperatively, AI-driven integration of multimodal data including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms facilitates early detection of comp
This review outlines current applications of artificial intelligence across the perioperative cancer pathway in onco-anaesthesia: 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; postoperatively, AI-driven integration of multimodal data including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms facilitates early detection of comp
This review outlines current applications of artificial intelligence across the perioperative cancer pathway in onco-anaesthesia: 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; postoperatively, AI-driven integration of multimodal data including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms facilitates early detection of comp
This review outlines current applications of artificial intelligence across the perioperative cancer pathway in onco-anaesthesia: 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; postoperatively, AI-driven integration of multimodal data including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms facilitates early detection of comp