Automated Bliss: Transforming Anesthesia Care through Artificially Intelligent Drug Delivery

On This Episode:

Today’s episode explores the revolutionary landscape of automated drug delivery systems in anesthesia. The hosts unravel the intricacies of these technologies, discussing real-world examples and contemplating the integration of adaptive, intelligent computer systems.

Through AI-powered drug delivery systems, we are witnessing a paradigm shift in how we approach anesthesia care. These systems not only make procedures more convenient but also enhance patient safety, reduce human error, and improve the overall efficiency of anesthesia delivery. By integrating artificial intelligence in anesthesia care, we can fine-tune the balance required during general anesthesia, managing analgesia, inducing hypnosis, and suppressing somatic and autonomic responses to noxious stimuli with greater precision.

As healthcare professionals, we know that achieving the desired clinical effect in anesthesia is not just about administering a dose. It’s about hitting the sweet spot, that therapeutic concentration at the precise site of action while minimizing side-effects. Through AI-powered drug delivery systems, we can achieve this balance with greater accuracy, improving patient outcomes and reducing the risk of complications.

So, whether you’re a seasoned practitioner or simply curious about the convergence of medicine and technology, this podcast episode serves as an insightful gateway to the forefront of precision anesthesia. Join the conversation and stay informed about the transformative potential of automated drug delivery in anesthesia.

 

Here’s some of what we discuss in this episode:

  • An overview of what we mean by automated drug delivery systems.
  • How is AI influencing how we administer anesthesia, and how does it impact patient outcomes?
  • Controlling the delicate balance between nociception and antinociception poses unique challenges.
  • The sophisticated control methods shaping the future of closed-loop systems.

 

References

Alshawwa, S. Z., Kassem, A. A., Farid, R. M., Mostafa, S. K., & Labib, G. S. (2022). Nanocarrier Drug Delivery Systems: Characterization, Limitations, Future Perspectives and Implementation of Artificial Intelligence. Pharmaceutics14(4), 883-. https://doi.org/10.3390/pharmaceutics14040883

Avital, G., Snider, E. J., Berard, D., Vega, S. J., Hernandez Torres, S. I., Convertino, V. A., Salinas, J., & Boice, E. N. (2022). Closed-Loop Controlled Fluid Administration Systems: A Comprehensive Scoping Review. Journal of Personalized Medicine12(7), 1168-. https://doi.org/10.3390/jpm12071168

Fawcett, W. J., & Klein, A. A. (2021). Anaesthesia and peri‐operative medicine over the next 25 years. Anaesthesia76(10), 1416–1420. https://doi.org/10.1111/anae.15552

Ghita, M., Neckebroek, M., Muresan, C., & Copot, D. (2020). Closed-Loop Control of Anesthesia: Survey on Actual Trends, Challenges and Perspectives. IEEE Access8, 206264–206279. https://doi.org/10.1109/ACCESS.2020.3037725

Gonzalez-Cava, J. M., Arnay, R., León, A., Martín, M., Reboso, J. A., Calvo-Rolle, J. L., & Mendez-Perez, J. A. (2020). Machine learning based method for the evaluation of the Analgesia Nociception Index in the assessment of general anesthesia. Computers in Biology and Medicine118, 103645–103645. https://doi.org/10.1016/j.compbiomed.2020.103645

Gray, G. M., Ahumada, L. M., Rehman, M. A., Varughese, A., Fernandez, A. M., Fackler, J., Yates, H. M., Habre, W., Disma, N., & Lonsdale, H. (2023). A machine‐learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset. Pediatric Anesthesia33(9), 710–719. https://doi.org/10.1111/pan.14694

Jamali, N., Sadegheih, A., Lotfi, M. M., Wood, L. C., & Ebadi, M. J. (2021). Estimating the Depth of Anesthesia During the Induction by a Novel Adaptive Neuro-Fuzzy Inference System: A Case Study. Neural Processing Letters53(1), 131–175. https://doi.org/10.1007/s11063-020-10369-7

Naaz, S., & Asghar, A. (2022). Artificial intelligence, nano-technology and genomic medicine: The future of anaesthesia. Journal of Anaesthesiology, Clinical Pharmacology38(1), 11–17. https://doi.org/10.4103/joacp.JOACP_139_20

Sharma, R., Singh, D., Gaur, P., & Joshi, D. (2021). Intelligent automated drug administration and therapy: future of healthcare. Drug Delivery and Translational Research11(5), 1878–1902. https://doi.org/10.1007/s13346-020-00876-4

Naaz, S., & Asghar, A. (2022). Artificial intelligence, nano-technology and genomic medicine: The future of anaesthesia. Journal of Anaesthesiology, Clinical Pharmacology38(1), 11–17. https://doi.org/10.4103/joacp.JOACP_139_20

Schamberg, G., Badgeley, M., Meschede-Krasa, B., Kwon, O., & Brown, E. N. (2022). Continuous action deep reinforcement learning for propofol dosing during general anesthesia. Artificial Intelligence in Medicine123, 102227–102227. https://doi.org/10.1016/j.artmed.2021.102227

Singh, M., & Nath, G. (2022). Artificial intelligence and anesthesia: A narrative review. Saudi Journal of Anaesthesia16(1), 86–93. https://doi.org/10.4103/sja.sja_669_21

Tacke, M., Kochs, E. F., Mueller, M., Kramer, S., Jordan, D., & Schneider, G. (2020). Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia. PloS One15(8), e0238249–e0238249. https://doi.org/10.1371/journal.pone.0238249

Wehbe, M. , Giacalone, M. & Hemmerling, T. M.  (2014).  Robotics and regional anesthesia.  Current Opinion in Anaesthesiology,  27 (5),  544-548. doi: 10.1097/ACO.0000000000000117.

Yun, W. J., Shin, M., Jung, S., Ko, J., Lee, H.-C., & Kim, J. (2023). Deep reinforcement learning-based propofol infusion control for anesthesia: A feasibility study with a 3000-subject dataset. Computers in Biology and Medicine156, 106739–106739. https://doi.org/10.1016/j.compbiomed.2023.106739

Johnson, K.B. and Talmage, E.D. (2023). In total intravenous anesthesia we trust: Building confidence in total intravenous anesthesia techniques. AnesthAnalg, 137, (3), 559-564.

Schnider, T.W., Nieuwenhuijs-Moeke, G.J., Beck-Schimmer, B., Hemmerling, T.M. (2023), Pro-Con debate: Should all general anesthesia be done using target controlled propofol infusion guided by objective monitoring of depth of anesthesia? AnesthAnalg, 137, (3), 565-575.

 

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