Jundishapur Scientific Medical Journal

Jundishapur Scientific Medical Journal

The Role of Artificial Intelligence in Optimizing Intraoperative Anesthesia: A Narrative Review

Document Type : Review

Authors
Neda Rahimian 1
Mahboobeh Hosseini 2
Ali Moshki 3
Seyedeh Zahra Nahardani 4
1 Associate Professor of Internal Medicine, Department of internal medicine, School of medicine, Firoozgar General Hospital. Iran University of Medical Sciences, Tehran, Iran
2 Ph.D. Student of Medical Education, Centre for Educational Research in Medical Sciences (CERMS), Department of Medical Education, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
3 Department of Anesthesiology and Pain Medicine, Hashemi-Nejad Hospital, Iran University of Medical Science, Tehran. Iran.
4 Assistant Professor of Medical Education, Ph.D, Center for Educational Research in Medical Sciences (CERMS), Iran University of Medical Sciences, Tehran, Iran.
10.22118/jsmj.2025.497970.3786
Abstract
Background and Objectives Artificial intelligence plays a crucial role in predicting patient conditions, optimizing drug doses, monitoring clinical status, and providing clinical decision support during anesthesia. This narrative review examines the applications of artificial intelligence in the field of anesthesia.
Subjects and Methods This review article was conducted in 2023 [Solar Hijri 1402] by searching international databases, including Scopus, PubMed, IEEE Xplore, and Web of Science, as well as Iranian databases such as IranDoc and SID, for the period 2019–2024.
Results The applications of artificial intelligence in anesthesia include the Infuse-OR system, which operates via magnetic coding, and Stanpump software, designed to automate the anesthetic process and reduce reliance on manual clinical intervention. Furthermore, machine learning models are utilized to determine anesthetic dosages by analyzing input datasets and patient conditions. Neural network algorithms are specifically applied for dose optimization, while Long Short-Term Memory (LSTM) networks are employed to analyze and predict anesthetic performance. For signal classification during anesthesia, a Convolutional Neural Network (CNN) architecture is utilized, consisting of an initial layer with 32 convolutional filters and max-pooling, a second convolutional layer with 32 filters followed by a secondary pooling layer, and a final fully connected layer.
Conclusion Integrating artificial intelligence into anesthesia enhances the quality of medical care, improves diagnostic accuracy, and increases patient safety. However, this integration also presents several challenges such as protecting patient privacy, ensuring responsible AI decision-making, preventing discrimination, improving the transparency of algorithms, clarifying physician responsibility, and addressing the impact of artificial intelligence on the role of physicians.
Keywords
Anesthesia
Artificial intelligence
Machine learning
Decision support systems
Neural networks
Perioperative medicine
Subjects
1.Singh M, Nath G. Artificial intelligence and anesthesia: A narrative review. Saudi journal of anaesthesia. 2022 Jan 1;16(1):86-93.[ 10.4103/sja.sja_669_21 ][PMID]
2.Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology. 2020 Feb;132(2):379.[ 10.1097/ALN.0000000000002960 ][PMID]
3. Alexander JC, Romito BT, Çobanoğlu MC. The present and future role of artificial intelligence and machine learning in anesthesiology. International anesthesiology clinics. 2020 Oct 1;58(4):7-16. [10.1097/AIA.0000000000000294 ][PMID]
4.Connor CW. Artificial intelligence and machine learning in anesthesiology. Anesthesiology. 2019 Dec;131(6):1346. [10.1097/ALN.0000000000002694 ][PMID]
5.Bowness J, Varsou O, Turbitt L, Burkett‐St Laurent D. Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound‐guided regional anesthesia. Clinical Anatomy. 2021 Jul;34(5):802-9. [10.1002/ca.23742 ][PMID]
6.Wang X, Liu J, Zuo YX, Zhu QM, Wei XC, Zou XH, Luo AL, Zhang FX, Li YL, Zheng H, Li H. Effects of ciprofol for the induction of general anesthesia in patients scheduled for elective surgery compared to propofol: a phase 3, multicenter, randomized, double-blind, comparative study. European Review for Medical & Pharmacological Sciences. 2022 Mar 1;26(5). [10.26355/eurrev_202203_28228 ][PMID]
7. Obara S, Hirata N, Hagihira S, Yoshida K, Kotake Y, Takagi S, Masui K. What are standard monitoring devices for anesthesia in future?. Journal of Anesthesia. 2024 Aug;38(4):537-41. [10.1007/s00540-024-03347-z ][PMID]
8. Guo W, Zang Q, Xu B, Xu T, Chen Z, Zhou M. Progress of artificial intelligence in anesthesia and perioperative medicine. Perioper Precis Med. 2024;2(1):1-0.
9. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointestinal endoscopy. 2020 Oct 1;92(4):807-12. [10.1016/j.gie.2020.06.040 ][PMID]
10.Szolovits P. Artificial intelligence and medicine. InArtificial intelligence in medicine 2019 Mar 13 (pp. 1-19). Routledge.
11.Briganti G, Le Moine O. Artificial intelligence in medicine: today and tomorrow. Frontiers in medicine. 2020 Feb 5;7:509744. [10.3389/fmed.2020.00027 ][PMID]
12. Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of ambient intelligence and humanized computing. 2023 Jul;14(7):8459-86. [10.1007/s12652-021-03612-z ][PMID]
13.Mirbabaie M, Stieglitz S, Frick NR. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health and Technology. 2021 Jul;11(4):693-731.
14.Shen J, Zhang CJ, Jiang B, Chen J, Song J, Liu Z, He Z, Wong SY, Fang PH, Ming WK. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR medical informatics. 2019 Aug 16;7(3):e10010. [10.2196/10010 ][PMID]
15.Subramanian M, Wojtusciszyn A, Favre L, Boughorbel S, Shan J, Letaief KB, Pitteloud N, Chouchane L. Precision medicine in the era of artificial intelligence: implications in chronic disease management. Journal of translational medicine. 2020 Dec;18(1):472. [10.1186/s12967-020-02658-5 ][PMID]
16.Liu X, McGrath S, Flanagan C, Lei Y, Zeng L. Perioperative Anesthesia Data: Visualization, Effects, Analysis with Artificial Intelligence. In2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB) 2024 Apr 19 (pp. 746-751). IEEE.
17.De Cassai A, Dost B. Concerns regarding the uncritical use of ChatGPT: a critical analysis of AI-generated references in the context of regional anesthesia. Regional anesthesia and pain medicine. 2024 May 1;49(5):378-80. [10.1136/rapm-2023-104771 ][PMID]
18.Bowness J, El‐Boghdadly K, Burckett‐St Laurent D. Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia. Anaesthesia. 2021 May;76(5):602-7. [10.1111/anae.15212 ][PMID]
19. McKendrick M, Yang S, McLeod GA. The use of artificial intelligence and robotics in regional anaesthesia. Anaesthesia. 2021 Jan;76:171-81. [10.1111/anae.15274 ][PMID]
20.Shimizu Y, Saeki N, Ohshimo S, Doi M, Oue K, Yoshida M, Takahashi T, Oda A, Sadamori T, Tsutsumi YM, Shime N. Usefulness of new acoustic respiratory sound monitoring with artificial intelligence for upper airway assessment in obese patients during monitored anesthesia care. The Journal of Medical Investigation. 2023;70(3.4):430-5. [10.2152/jmi.70.430 ][PMID]
21.Sadeghian Shahi MR. Skeletal muscle fatigue and increased endurance time with breathing heliox in healthy humans.
22. Shalbaf A, Saffar M, Sleigh JW, Shalbaf R. Monitoring the depth of anesthesia using a new adaptive neurofuzzy system. IEEE journal of biomedical and health informatics. 2017 May 29;22(3):671-7. [10.1109/JBHI.2017.2709841 ][PMID]
23.Jeong YS, Kang AR, Jung W, Lee SJ, Lee S, Lee M, Chung YH, Koo BS, Kim SH. Prediction of blood pressure after induction of anesthesia using deep learning: A feasibility study. Applied Sciences. 2019 Nov 27;9(23):5135.
24.Kang AR, Lee J, Jung W, Lee M, Park SY, Woo J, Kim SH. Development of a prediction model for hypotension after induction of anesthesia using machine learning. PloS one. 2020 Apr 16;15(4):e0231172. [10.1371/journal.pone.0231172 ][PMID]
25.Li R, Wu Q, Liu J, Wu Q, Li C, Zhao Q. Monitoring depth of anesthesia based on hybrid features and recurrent neural network. Frontiers in neuroscience. 2020 Feb 7;14:26. [10.3389/fnins.2020.00026 ][PMID]
26.Park Y, Han SH, Byun W, Kim JH, Lee HC, Kim SJ. A real-time depth of anesthesia monitoring system based on deep neural network with large EDO tolerant EEG analog front-end. IEEE Transactions on Biomedical Circuits and Systems. 2020 May 28;14(4):825-37. [10.1109/TBCAS.2020.2998172 ][PMID]
 
 
27.Wingert T, Lee C, Cannesson M. Machine learning, deep learning, and closed loop devices—anesthesia delivery. Anesthesiology clinics. 2021 Sep 1;39(3):565-81.[ 10.1016/j.anclin.2021.03.012 ][PMID]
28.Wang Y, Lei L, Ji M, Tong J, Zhou CM, Yang JJ. Predicting postoperative delirium after microvascular decompression surgery with machine learning. Journal of clinical anesthesia. 2020 Nov 1;66:109896. [10.1016/j.jclinane.2020.109896 ][PMID]
29.Cascella M, Tracey MC, Petrucci E, Bignami EG. Exploring artificial intelligence in anesthesia: a primer on ethics, and clinical applications. Surgeries. 2023 May 29;4(2):264-74.
30.Gandotra S, Gupta S. Challenges to AI use in anesthesia and healthcare: An anesthesiologist's perspective. Indian Journal of Clinical Anaesthesia. 2023;10(4):371-5.
31.Bsisu I, Alqassieh R, Aloweidi A, Abu-Humdan A, Subuh A, Masarweh D. Attitudes of Jordanian Anesthesiologists and Anesthesia Residents towards Artificial Intelligence: A Cross-Sectional Study. Journal of Personalized Medicine. 2024 Apr 25;14(5):447. [10.3390/jpm14050447 ][PMID]
32.Singam A. Revolutionizing patient care: a comprehensive review of artificial intelligence applications in anesthesia. Cureus. 2023 Dec 4;15(12). [10.7759/cureus.49887 ][PMID]

  • Receive Date 04 January 2025
  • Revise Date 27 October 2025
  • Accept Date 04 November 2025