Artificial intelligence (AI) is rapidly transforming anesthesia delivery systems, bringing significant advancements in safety, precision, and workflow efficiency. One of the most notable developments is the emergence of smart anesthesia systems that use AI to automate and optimize drug administration. These systems rely on real-time physiological data—such as bispectral index (BIS), blood pressure, and heart rate—to maintain an appropriate depth of anesthesia. Closed-loop systems, for example, can automatically adjust infusion rates for agents like propofol and remifentanil in response to ongoing patient data.
McSleepy is one of the earliest examples of a fully automated anesthesia delivery system, developed in 2008 by researchers at McGill University in Canada. Designed as a proof of concept, McSleepy manages all three components of general anesthesia—hypnosis, analgesia, and muscle relaxation—without manual intervention. It uses a closed-loop feedback system that continuously analyzes BIS, train-of-four (TOF) nerve stimulation, and vital signs such as heart rate and oxygen saturation. Based on this data, it administers propofol for sedation, remifentanil for analgesia, and a neuromuscular blocking agent like rocuronium for paralysis. Controlled by real-time algorithms, McSleepy demonstrated that AI could maintain stable, safe anesthesia with minimal clinician input, paving the way for future AI-driven systems.
A more recent advancement, iControl-RP, was developed in France and focuses specifically on managing sedation and analgesia during general anesthesia. It uses BIS monitoring and pharmacokinetic models to control the infusion of propofol and remifentanil through a dual-loop mechanism. Each drug is managed by its own closed-loop algorithm that continuously adapts to patient-specific responses. In clinical trials, iControl-RP has maintained anesthesia depth more consistently than manual methods, while sometimes reducing drug use and speeding recovery. Though not yet widely adopted, iControl-RP represents a meaningful step toward practical AI-assisted anesthesia in the operating room.
Beyond drug delivery, AI is enhancing clinical decision-making. Tools like the Hypotension Prediction Index (HPI) use machine learning to analyze arterial waveform data and predict hypotensive episodes before they occur. Similarly, the Analgesia Nociception Index (ANI) evaluates heart rate variability to help guide opioid dosing. AI-enhanced monitoring systems also reduce alarm fatigue by filtering false positives and improving the signal-to-noise ratio in the OR.
AI is making strides in airway management as well. Devices like the C-MAC video laryngoscope are being enhanced with AI features that detect vocal cords and guide intubation. Experimental robotic intubation systems are also in development, offering precision-guided airway access using real-time video analysis. Educational tools such as Airway AI use deep learning to evaluate technique in laryngoscopy training videos and provide real-time feedback to learners.
AI tools are also streamlining operating room logistics. Predictive analytics assist with case duration forecasting, staff scheduling, and turnover management. Postoperatively, AI helps monitor patients for respiratory depression, pain, or agitation and can support timely discharge decisions based on real-time trends. AI is even improving education and training through adaptive simulation platforms and assisting regional anesthesia by enhancing ultrasound image interpretation and anatomical recognition.
Despite its many advantages, integrating AI into anesthesia practice presents several challenges. Questions of legal liability remain, particularly when AI-guided decisions contribute to adverse outcomes. Clinicians also express concern about transparency, preferring systems whose reasoning can be clearly explained. Moreover, the effectiveness of AI depends heavily on the quality of the data it is trained on, and regulatory frameworks are still evolving to govern its use.
Summary Table
Application Area |
AI Tool or System |
Function |
| Airway Management | AI-assisted laryngoscopy, robotic intubation | Enhance safety and success in airway access |
| Hemodynamic Prediction | Hypotension Prediction Index (HPI) | Predict intraoperative hypotension |
| Analgesia Monitoring | ANI, nociception indices + AI | Optimize intraoperative opioid dosing |
| Drug Delivery | Closed-loop propofol/remifentanil systems | Real-time anesthesia titration |
| EEG Interpretation | SedLine, AI EEG analyzers | Advanced sedation and depth-of-anesthesia tracking |
| Workflow Optimization | OR scheduling AI (e.g., Clew, Qventus) | Improve OR efficiency and throughput |
| Decision Support | AI in EMR/AIMS systems | Risk prediction and care guidance |
Overall, AI will not replace anesthesia providers, but it will significantly enhance their capabilities by supporting better decision-making, improving safety, and increasing efficiency. As the field evolves, anesthesia professionals who adopt AI tools will be well-positioned to lead in an increasingly digital healthcare environment. Still, it is essential to recognize that the human factor in patient care remains irreplaceable. Compassion, intuition, communication, and ethical judgment are qualities that only skilled anesthesia providers can offer. The future of anesthesia will be defined by a partnership between advanced technology and the enduring art of human-centered care.