Colorado Anesthesia Services Group

Robots in Anesthesia 

Autonomous systems, or robots, are being increasingly integrated into medicine. Automation can provide precision therapy with a high degree of reproducibility, making it highly appealing to anesthesiologists.1 Considering the ubiquity of human error stemming from factors like fatigue, boredom, and bias, there is a strong rationale for the role automation can play in supporting clinical care. Additionally, robots can take care of smaller, simpler tasks, while physicians focus their attention on tasks requiring human intelligence, emotion, and judgment. Currently, robots in the field of anesthesia can be categorized into three main types: pharmacological, mechanical, and cognitive.2 The first two groups are meant to aid in either anesthesia delivery or manual tasks, while the third group refers to autonomous systems that are “intelligent;” that is, they can offer recommendations related to specific clinical scenarios. 

Pharmacological robots in anesthesia are designed to maintain patient homeostasis while altering specific parameters of interest, such as blood pressure, heart rate, or blood flow.2 One of the main principles behind pharmacological robots is closed-loop control. This involves delivering anesthetic agents through an actuator, monitoring the effects of these agents on the body in real time, and using algorithms in a “closed-loop brain” to adjust the dosage appropriately.3 The Closed-Loop Anesthesia Delivery System (CLADS) is a device that has been examined for its ability to provide closed-loop control. Patients undergoing elective surgery (n=40) under general anesthesia were randomized to two groups, one who received propofol administered by the CLADS (with anesthesiologist oversight), and another who received anesthesia delivered manually.4 Results indicated patients in the CLADS group had less overshoot of target bi-spectral index (BIS) values, which is a measure of brain activity used to estimate level of anesthesia.5 The closed-loop system also maintained BIS values within a target range for a significantly longer time period than manual administration. Smaller doses of propofol were required for anesthesia induction and there was faster postoperative recovery for CLADS patients.4

Manual, or mechanical, robots are designed to assist the anesthesiologist in various tasks, such as endotracheal intubation and regional anesthesia delivery. The first description of a simulated robotically assisted fiberoptic intubation procedure was provided by Tighe et al. in 2010. The DaVinci system, which was used on a mannequin, incorporated 4 separate robotic arms, one of which was connected to a stereoscopic camera.6 A year later, the Kepler Intubation System (KIS) simplified the DaVinci system for use in humans. In a pilot study of 12 patients, the KIS performed endotracheal intubation with a success rate of 91%.7 More recently, a research group in China developed a mechanical robot. The researchers demonstrated practitioners with little to no experience could perform intubation with a higher first-pass rate and overall success rate with the robot system than standard direct laryngoscopy.8 

Cognitive robots are designed to provide support to the clinician regarding decision-making. These systems analyze various patient data to offer immediate clinical suggestions or reminders, as well as treatment options for long-term care. They serve as cognitive aids for the clinician who must then weigh and implement the suggestions. Conversely, a closed-loop system works autonomously and does not require human decisions. Cognitive robots can be useful in both preoperative and intraoperative settings. Preoperatively, automated smart alarms can be a helpful tool for early detection of abnormal laboratory values. In addition, it has been demonstrated that these cognitive robots can ensure drug administration compliance; for example, they can remind patients to continue taking their prescription beta-blockers before scheduled cardiovascular surgery. Intraoperatively, automated alarms can identify the appropriate prophylactic antibiotics and recommend dosing levels to ensure improved surgical outcome. Artificial intelligence systems can also use expert knowledge to provide suggestions in the high-pressure, fast-paced operating room to lower the rate of false alarms.3

The integration of robots in anesthesia represents a significant advancement in enhancing patient care and optimizing clinical outcomes. Pharmacological automation ensures precise and consistent drug delivery, reducing variability and human error. Manual systems can streamline routine tasks, allowing physicians to focus on more cognitive endeavors. Cognitive robots use sophisticated algorithms to improve decision-making processes and support clinicians. As these innovations continue to progress, further research is essential to addressing potential challenges, validating long-term benefits, and ensuring safety and efficacy in diverse clinical settings. Continued exploration will allow clinicians to fully realize the potential of autonomous systems in the field of anesthesia. 

References

  1. Zaouter, Cédrick, et al. “Feasibility of Automated Propofol Sedation for Transcatheter Aortic Valve Implantation: A Pilot Study.” Anesthesia & Analgesia, vol. 125, no. 5, Nov. 2017, pp. 1505–12. https://doi.org/10.1213/ANE.0000000000001737 
  2. Hemmerling, Thomas M., et al. “Robotic Anesthesia – A Vision for the Future of Anesthesia.” Translational Medicine @ UniSa, vol. 1, Oct. 2011, pp. 1–20. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3728848/ 
  3. Zaouter, Cédrick, et al. “Autonomous Systems in Anesthesia: Where Do We Stand in 2020? A Narrative Review.” Anesthesia & Analgesia, vol. 130, no. 5, May 2020, pp. 1120–32. https://doi.org/10.1213/ANE.0000000000004646 
  4. Puri, G. D., et al. “Closed-Loop Anaesthesia Delivery System (CLADSTM) Using Bispectral Index: A Performance Assessment Study.” Anaesthesia and Intensive Care, vol. 35, no. 3, June 2007, pp. 357–62. https://doi.org/10.1177/0310057X0703500306
  5. Mathur, Surbhi, et al. “Bispectral Index.” StatPearls, StatPearls Publishing, 2024. http://www.ncbi.nlm.nih.gov/books/NBK539809/  
  6. Tighe, Patrick J., et al. “Robot-Assisted Airway Support: A Simulated Case.” Anesthesia & Analgesia, vol. 111, no. 4, Oct. 2010, pp. 929–31. https://doi.org/10.1213/ANE.0b013e3181ef73ec 
  7. Hemmerling, T. M., et al. “First Robotic Tracheal Intubations in Humans Using the Kepler Intubation System.” British Journal of Anaesthesia, vol. 108, no. 6, June 2012, pp. 1011–16. https://doi.org/10.1093/bja/aes034 
  8. Wang, Xinyu, et al. “An Original Design of Remote Robot-Assisted Intubation System.” Scientific Reports, vol. 8, no. 1, Sept. 2018, p. 13403. https://doi.org/10.1038/s41598-018-31607-y