Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1498 2023-06-17 07:50:03 |
2 format correct Meta information modification 1498 2023-06-19 05:07:26 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Cascella, M.; Tracey, M.C.; Petrucci, E.; Bignami, E.G. Applications of Artificial Intelligence in Anesthesia. Encyclopedia. Available online: https://encyclopedia.pub/entry/45740 (accessed on 15 June 2024).
Cascella M, Tracey MC, Petrucci E, Bignami EG. Applications of Artificial Intelligence in Anesthesia. Encyclopedia. Available at: https://encyclopedia.pub/entry/45740. Accessed June 15, 2024.
Cascella, Marco, Maura C. Tracey, Emiliano Petrucci, Elena Giovanna Bignami. "Applications of Artificial Intelligence in Anesthesia" Encyclopedia, https://encyclopedia.pub/entry/45740 (accessed June 15, 2024).
Cascella, M., Tracey, M.C., Petrucci, E., & Bignami, E.G. (2023, June 17). Applications of Artificial Intelligence in Anesthesia. In Encyclopedia. https://encyclopedia.pub/entry/45740
Cascella, Marco, et al. "Applications of Artificial Intelligence in Anesthesia." Encyclopedia. Web. 17 June, 2023.
Applications of Artificial Intelligence in Anesthesia
Edit

The field of anesthesia has always been at the forefront of innovation and technology, and the integration of Artificial Intelligence (AI) represents the next frontier in anesthesia care. The use of AI and its subtypes, such as machine learning, has the potential to improve efficiency, reduce costs, and ameliorate patient outcomes. AI can assist with decision making, but its primary advantage lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. The potential uses of AI in anesthesia can be schematically grouped into clinical decision support and pharmacologic and mechanical robotic applications.

artificial intelligence machine learning anesthesia algorithms

1. Introduction

The field of anesthesia has always been at the forefront of innovation and technology [1]. From the introduction of ether in the mid-19th century to the development of modern anesthetic agents, anesthesia care has continuously evolved to improve patient outcomes [2]. As a result of technological progress, anesthesia has become increasingly safe, precise, and tailored to individual patient needs.
Artificial intelligence (AI) is an emerging technology that has the potential to revolutionize healthcare delivery [3]. AI refers to the development of computer systems that can perform tasks that would usually require human intelligence, such as learning, reasoning, problem solving, decision making, and perception. On the other hand, Machine Learning (ML) is a subset of AI that focuses on enabling machines to learn from data without being explicitly programmed. ML algorithms can analyze data, learn from it, and make predictions or decisions based on that learning. Deep learning is a subset of ML that involves training artificial neural networks with multiple layers to recognize patterns in data. It is used in image and speech recognition, natural language processing, and other applications. Other AI subsets are robotics, computer vision, and expert systems that are designed to mimic the decision-making abilities of a human expert in a particular domain [4].
The integration of AI and ML represents the next frontier in anesthesia care, with the potential to further improve efficiency, reduce costs, and enhance patient safety [4]. This technology is being increasingly used in various aspects of anesthesia care, including preoperative evaluation, intraoperative monitoring, and postoperative management. For example, predictive algorithms can be used to forecast patient responses to anesthesia, allowing clinicians to optimize dosing and minimize the risk of adverse events [5]. Automated systems can also monitor vital signs during surgery and alert clinicians when intervention is required [6]. Additionally, AI can be used to identify patient-specific risk factors for postoperative complications, enabling clinicians to take proactive steps to reduce complications [7].
Interestingly, in the field of anesthesiology, AI can also offer virtual simulations and training programs for enhancing the skills and knowledge of anesthesiologists. Multiple studies have demonstrated the efficacy of AI in assisting in the evaluation of trainee performance in anesthesia. For instance, interesting perspectives regard the application of AI techniques to increase learning efficiency in the field of ultrasound-guided regional anesthesia [8].

2. AI in Anesthesia

AI involves the development of algorithms that can learn and improve from data, for helping clinicians in the decision-making process. In anesthesia, AI algorithms can assist anesthesiologists in making clinical decisions in different contexts. However, the primary advantage of AI lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. For example, AI has been used for presurgical evaluation [9][10][11], to predict patient responses to anesthesia [12], to automate drug dosing [13], to monitor patients during surgery [6], and other purposes [14][15][16][17][18][19][20][21][22][23][24][25] (Table 1).
Table 1. Potential applications of AI in anesthesia.
Application Strategy [Refs.]
Presurgical Evaluation Preoperative management and planning. [12][26]
Anesthetic Dosage Optimization AI algorithms can analyze a patient’s medical history, physiological data, and other factors to determine the most appropriate anesthetic dose, which can improve patient safety and minimize complications. [6][13]
Preventing Drug Errors AI-powered systems can help prevent drug administration errors by verifying medication orders, drug interactions, and dosages. [14][15]
Early Detection of Complications AI can be used to analyze various data streams in real time to detect patterns or anomalies that may indicate a potential complication, such as hypoxia and hypotension. [4][16]
Predicting Patient Outcomes Machine learning algorithms can be used to predict the likelihood of complications or adverse outcomes during surgery, allowing anesthesiologists to make more informed decisions about patient care. [10]
Predicting Postsurgical ICU Admission Various machine learning algorithms can be employed to develop predictive models from data, such as patient demographics, medical history, surgical procedures, vital signs, laboratory values, and other clinical factors. [11]
Automated Monitoring AI-powered monitoring systems can continuously track a patient’s vital signs, such as heart rate, blood pressure, and oxygen saturation, and alert the anesthesiologist to any potential problems. [16]
Automated Anesthesia Dosing AI can help tailor anesthesia delivery and optimize closed-loop systems based on patient data. [4][6]
Pain Management AI can help adjust the dosage and administering of drugs to achieve optimal postoperative pain control. [17][18]
Real-Time Decision Support AI algorithms can provide real-time decision support to anesthesiologists during surgery, such as recommending alternative drug options if a patient is not responding to the initial anesthetic. [19]
Postoperative Monitoring AI can help monitor patients postoperatively, predicting and detecting any adverse events that might occur, and giving alerts and recommendations to physicians. [20]
Resource Allocation AI algorithms can help optimize the use of anesthesia resources by identifying the best candidates for specific procedures and reducing unnecessary anesthesia use. [21]
Training and Education AI can be used to simulate scenarios and train anesthesiologists to handle challenging situations, such as unexpected complications during surgery. [22][23]
Image Recognition AI algorithms can analyze medical images to identify anatomical structures and guide the placement of regional anesthesia techniques. [8]
Research and Analysis AI can help analyze vast amounts of data from anesthesia records, patient charts, and other sources to identify patterns, trends, and insights that could help improve patient outcomes and safety. [24][25]
Although with limitations due to classification difficulties, the applications of AI in anesthesia can be schematically summarized into Clinical Decision Support System (CDSS) applications and pharmacological and mechanical robotic systems. Furthermore, tele-anesthesia includes strategies of telemedicine (preoperative assessment and post-operative monitoring), as well as device networking for improving logistics in the operating room and augmented reality approaches for training and assistance (Figure 1). AI can also be used to reduce costs by automating tasks that are currently performed by anesthesiologists.
Figure 1. Infographic of potential modalities for AI-based applications in anesthesia. AI-powered Clinical Decision Support Systems can help anesthesiologists in decision-making processes by analyzing patient data and providing real-time recommendations. Pharmacological robotic methods (closed-loop anesthesia delivery systems) can be used to deliver anesthesia, monitor vital signs, and adjust the dosage of medication as needed (tailored anesthesia delivery). Attempts with mechanical robots have been performed for intubation and nerve blocks. Tele-anesthesia includes strategies of telemedicine, such as preoperative assessment, postoperative monitoring, and remote consultations. This approach can help expand access to anesthesia services and improve patient outcomes, particularly in underserved areas. Created with BioRender.com (accessed on 18 April 2023).
Specifically, CDSS in anesthesia is a computer-based tool that assists anesthesia providers in making clinical decisions by providing real-time patient-specific recommendations and alerts based on patient data and medical knowledge. It can aid in the optimization of anesthesia care by offering automated recommendations on the dosing of anesthetic agents, fluid management, and other aspects of perioperative care. These systems can also help to identify potential drug interactions, allergies, and patient comorbidities that may impact the provision of anesthesia [27]. Predictive algorithms of CDSS can also be implemented to detect early signs of deterioration in different post-anesthesia scenarios [28]. Furthermore, artificial neural network systems have been employed to develop a predictive model for the duration of surgery and anesthesia emergence. This process aims to optimize anesthesia care and streamline the utilization of operating rooms [29].
Robotics represents an additional area where AI finds application within the field of anesthesia. A robot refers to a mechanical system that has the ability to engage with the environment through deliberate actions and interventions. Robotic systems in anesthesia can be grouped into pharmacological robots and mechanical robots. Pharmacological robots are devices that process continuously collected multiple clinical and biosignal data from hypnosis, analgesia, muscle relaxation, and other vital signs to ultimately predict the appropriate anesthesia dosage based on the patient’s individual needs [30]. This complex input–output process structures the so-called closed-loop anesthesia delivery system (CLAD). For example, the McSleepy intravenous sedation device was developed to automatically administer propofol, analgesics, and muscle relaxants [31].
Clinical experience with mechanical robots in anesthesia is limited. Some authors have proposed an intubation system using the Da Vinci surgical system or other approaches [32]. Interesting scenarios concern regional anesthesia, such as the Magellan robot used to perform peripheral nerve block [33]. Nevertheless, it seems that currently, robots lack the dexterity to perform these challenging anesthesia tasks.
AI-powered device networking can be used to improve logistics in the operating room by enabling seamless communication between anesthesia machines, patient monitors, and other medical devices. This can help streamline workflow, reduce errors, and improve patient safety. Augmented reality technology can be used for training and assistance in anesthesia. For example, it can provide real-time feedback during procedures, help train new anesthesiologists, and improve the accuracy of nerve blocks.

References

  1. Moon, J.S.; Cannesson, M. A Century of Technology in Anesthesia & Analgesia. Anesth. Analg. 2022, 135 (Suppl. S2), S48–S61.
  2. Petermann, H.; Böhrer, H.; Witte, W. Von der Äthernarkose zur “grünen” Anästhesie: Herausforderungen in der Anästhesiologie der letzten 175 Jahre . Anaesthesist 2021, 70, 832–842.
  3. Stanfill, M.H.; Marc, D.T. Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management. Yearb. Med. Inform. 2019, 28, 56–64.
  4. Bellini, V.; Valente, M.; Gaddi, A.V.; Pelosi, P.; Bignami, E. Artificial intelligence and telemedicine in anesthesia: Potential and problems. Minerva Anestesiol. 2022, 88, 729–734.
  5. Bihorac, A.; Ozrazgat-Baslanti, T.; Ebadi, A.; Motaei, A.; Madkour, M.; Pardalos, P.M.; Lipori, G.; Hogan, W.R.; Efron, P.A.; Moore, F.; et al. MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery. Ann. Surg. 2019, 269, 652–662.
  6. Lee, H.C.; Ryu, H.G.; Chung, E.J.; Jung, C.W. Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach. Anesthesiology 2018, 128, 492–501.
  7. Hofer, I.S.; Lee, C.; Gabel, E.; Baldi, P.; Cannesson, M. Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set. NPJ Digit. Med. 2020, 3, 58.
  8. Hewson, D.W.; Bedforth, N.M. Closing the gap: Artificial intelligence applied to ultrasound-guided regional anaesthesia. Br. J. Anaesth. 2023, 130, 245–247.
  9. Solanki, S.L.; Pandrowala, S.; Nayak, A.; Bhandare, M.; Ambulkar, R.P.; Shrikhande, S.V. Artificial intelligence in perioperative management of major gastrointestinal surgeries. World J. Gastroenterol. 2021, 27, 2758–2770.
  10. Li, Y.Y.; Wang, J.J.; Huang, S.H.; Kuo, C.L.; Chen, J.Y.; Liu, C.F.; Chu, C.C. Implementation of a machine learning application in preoperative risk assessment for hip repair surgery. BMC Anesthesiol. 2022, 22, 116.
  11. Chiew, C.J.; Liu, N.; Wong, T.H.; Sim, Y.E.; Abdullah, H.R. Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission. Ann. Surg. 2020, 272, 1133–1139.
  12. Corey, K.M.; Kashyap, S.; Lorenzi, E.; Lagoo-Deenadayalan, S.A.; Heller, K.; Whalen, K.; Balu, S.; Heflin, M.T.; McDonald, S.R.; Swaminathan, M.; et al. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study. PLoS Med. 2018, 15, e1002701.
  13. Neckebroek, M.; Ionescu, C.M.; van Amsterdam, K.; De Smet, T.; De Baets, P.; Decruyenaere, J.; De Keyser, R.; Struys, M.M.R.F. A comparison of propofol-to-BIS post-operative intensive care sedation by means of target controlled infusion, Bayesian-based and predictive control methods: An observational, open-label pilot study. J. Clin. Monit. Comput. 2019, 33, 675–686.
  14. Härkänen, M.; Haatainen, K.; Vehviläinen-Julkunen, K.; Miettinen, M. Artificial Intelligence for Identifying the Prevention of Medication Incidents Causing Serious or Moderate Harm: An Analysis Using Incident Reporters’ Views. Int. J. Environ. Res. Public Health 2021, 18, 9206.
  15. Syrowatka, A.; Song, W.; Amato, M.G.; Foer, D.; Edrees, H.; Co, Z.; Kuznetsova, M.; Dulgarian, S.; Seger, D.L.; Simona, A.; et al. Key use cases for artificial intelligence to reduce the frequency of adverse drug events: A scoping review. Lancet Digit. Health 2022, 4, e137–e148.
  16. Hashimoto, D.A.; Witkowski, E.; Gao, L.; Meireles, O.; Rosman, G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology 2020, 132, 379–394.
  17. Nagireddi, J.N.; Vyas, A.K.; Sanapati, M.R.; Soin, A.; Manchikanti, L. The Analysis of Pain Research through the Lens of Artificial Intelligence and Machine Learning. Pain Physician 2022, 25, E211–E243.
  18. Cascella, M.; Montomoli, J.; Bellini, V.; Bignami, E.G. Integrating data science and neural architecture techniques for automatic pain assessment in critically ill patients. Anaesth. Crit. Care Pain Med. 2023, 42, 101220.
  19. Cobianchi, L.; Piccolo, D.; Dal Mas, F.; Agnoletti, V.; Ansaloni, L.; Balch, J.; Biffl, W.; Butturini, G.; Catena, F.; Coccolini, F.; et al. Surgeons’ perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: Results from an international survey. World J. Emerg. Surg. 2023, 18, 1.
  20. McCoy, A.; Das, R. Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Qual. 2017, 6, e000158.
  21. Loftus, T.J.; Vlaar, A.P.J.; Hung, A.J.; Bihorac, A.; Dennis, B.M.; Juillard, C.; Hashimoto, D.A.; Kaafarani, H.M.A.; Tighe, P.J.; Kuo, P.C.; et al. Executive summary of the artificial intelligence in surgery series. Surgery 2022, 171, 1435–1439.
  22. O’Connor, S.; Chat, G.P. Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse? Nurse Educ. Pract. 2023, 66, 103537.
  23. Cascella, M.; Montomoli, J.; Bellini, V.; Bignami, E. Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. J. Med. Syst. 2023, 47, 33.
  24. Bellini, V.; Cascella, M.; Cutugno, F.; Russo, M.; Lanza, R.; Compagnone, C.; Bignami, E.G. Understanding basic principles of Artificial Intelligence: A practical guide for intensivists. Acta Biomed. 2022, 93, e2022297.
  25. Hassan, A.M.; Rajesh, A.; Asaad, M.; Nelson, J.A.; Coert, J.H.; Mehrara, B.J.; Butler, C.E. A Surgeon’s Guide to Artificial Intelligence-Driven Predictive Models. Am. Surg. 2023, 89, 11–19.
  26. Kovarik, C.L.; Sanabria, B.; Stoff, B.K. Image Consent and the Development of Image-Based Artificial Intelligence. JAMA Dermatol. 2022, 158, 589.
  27. Freundlich, R.E.; Ehrenfeld, J.M. Anesthesia information management: Clinical decision support. Curr. Opin. Anaesthesiol. 2017, 30, 705–709.
  28. Olsen, R.M.; Aasvang, E.K.; Meyhoff, C.S.; Dissing Sorensen, H.B. Towards an automated multimodal clinical decision support system at the post anesthesia care unit. Comput. Biol. Med. 2018, 101, 15–21.
  29. Huang, L.; Chen, X.; Liu, W.; Shih, P.C.; Bao, J. Automatic Surgery and Anesthesia Emergence Duration Prediction Using Artificial Neural Networks. J. Healthc. Eng. 2022, 2022, 2921775.
  30. Wingert, T.; Lee, C.; Cannesson, M. Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery. Anesthesiol. Clin. 2021, 39, 565–581.
  31. Wehbe, M.; Arbeid, E.; Cyr, S.; Mathieu, P.A.; Taddei, R.; Morse, J.; Hemmerling, T.M. A technical description of a novel pharmacological anesthesia robot. J. Clin. Monit. Comput. 2014, 28, 27–34.
  32. Hemmerling, T.M.; Wehbe, M.; Zaouter, C.; Taddei, R.; Morse, J. The Kepler intubation system. Anesth. Analg. 2012, 114, 590–594.
  33. O’Donnell, B.D.; O’Sullivan, O.; Gal-lagher, A.G.; Shorten, G.D. Robotic assistance with needle guidance. Br. J. Anaesth. 2015, 114, 708–709.
More
Information
Subjects: Anesthesiology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , ,
View Times: 413
Revisions: 2 times (View History)
Update Date: 19 Jun 2023
1000/1000
Video Production Service