Table 2.

Uses of artificial intelligence in anesthesia practice.[10]

Domain	Uses
Control of Anesthesia Delivery	Automated delivery of anesthesia by the machine based on the input of BIS and EEG
	Forecasting of drug pharmacokinetics to further improve the control of Infusions of neuromuscular blockade or other related drugs
	Control of mechanical ventilation
	To automate weaning from mechanical ventilation
Event Prediction	Predicts the hypnotic effect (as measured by BIS) of an induction bolus dose of Propofol
	Prediction of return of consciousness after general anesthesia
	Neural networks have also been used to predict the rate of recovery from neuromuscular blockade
	Prediction of hypotensive episodes postinduction or during spinal anesthesia
	To automate the classification of ASA status
	To define difficult laryngoscopy findings
	To identify respiratory depression during conscious sedation
	To assist in decision making for the optimal method of anesthesia in pediatric surgery
	To predict hypotension in the ICU setting by arterial waveform
	To predict morbidity, weaning from ventilation, clinical deterioration, mortality, or readmission and in the ICU setting by machine learning
	To detect sepsis in the ICU setting
Ultrasound Guidance	Differentiation of artery and vein with the help of convolution neural network
	Identification of vertebral level for epidural catheter placement
Pain Management	Prediction of opioid dosing
	Assessment of pain from functional magnetic resonance imaging data
	Development of nociception level index based on machine learning analysis of photoplethysmograms and skin conductance waveforms
Operating Room Logistics	Scheduling of operating room time
	Tracking movements and actions of anesthesiologists
	Prediction of the duration of an operation based on the team, type of operation, and a patient’s relevant medical history