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Artificial Intelligence-Supported Systems in Anesthesiology and Its Standpoint to Date—A Review

DOI: 10.4236/ojanes.2023.137014, PP. 140-168

Keywords: Artificial Intelligence, Anesthesiology, Machine Learning, Pharmacological Robot, Mechanical Robot, ChatGPT

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Abstract:

Artificial intelligence (AI) is the technique that enables computers to solve problems and perform tasks that traditionally require human intelligence. The availability of large amounts of medical data from electronic medical records and powerful modern microcomputers enables the development of AI in medicine. AI has proven its applicability in many different medical areas, such as drug discovery, diagnostic radiology and pathology, as well as interventional applications in cardiology and surgery. However, until today, AI is scarcely used in the clinical practice of anesthesiology. Although there has been a significant body of research published on AI applications for anesthesiology in the literature, the number of developed robot systems for commercial use or those ready for clinical trials remains limited. The limitations of AI systems are identified and discussed, which include incorrect medical data formatting, individual patient variability, the lack of ability of current AI systems, anesthesiologist inexperience in AI usage, system unreliability, unexplainable AI conclusions and strict regulations. In order to ensure anesthesiologists’ trust in AI systems and improve their implementation in daily practice, strict quality control of the systems and algorithms should be undertaken. Further, anesthesiology personnel should play an integral role in the development of AI systems before we are able to see more AI integration in clinical anesthesiology.

References

[1]  Beecher, H.K. (1941) The First Anesthesia Death with Some Remarks Suggested by It on the Fields of the Laboratory and the Clinic in the Appraisal of New Anesthetic Agents. Anesthesiology, 2, 443-449.
https://doi.org/10.1097/00000542-194107000-00008
[2]  Clifton, B. and Hotten, W. (1963) Deaths Associated with Anaesthesia. BJA: British Journal of Anaesthesia, 35, 250-259.
https://doi.org/10.1093/bja/35.4.250
[3]  Dinnick, O. and Patterson, R.L. (1966) Deaths Associated with Anaesthesia: Observations on 600 Cases. Survey of Anesthesiology, 10, 155-156.
https://doi.org/10.1097/00132586-196604000-00037
[4]  Edwards, G., Morton, H., Pask, E. and Wylie, W. (1956) Deaths Associated with Anaesthesia: A Report on 1,000 Cases. Anaesthesia, 11, 194-220.
https://doi.org/10.1111/j.1365-2044.1956.tb07975.x
[5]  Jenkins, S. (2021) Anaesthesia-Related Deaths Analysed. ANZCA Bulletin, 30, 30-31.
[6]  Kothari, D., Gupta, S., Sharma, C. and Kothari, S. (2010) Medication Error in Anaesthesia and Critical Care: A Cause for Concern. Indian Journal of Anaesthesia, 54, 187-192.
https://doi.org/10.4103/0019-5049.65351
[7]  Schulz, C.M., Krautheim, V., Hackemann, A., Kreuzer, M., Kochs, E.F. and Wagner, K.J. (2015) Situation Awareness Errors in Anesthesia and Critical Care in 200 Cases of a Critical Incident Reporting System. BMC Anesthesiology, 16, Article No. 4.
https://doi.org/10.1186/s12871-016-0172-7
[8]  Costin, L. (2022) Young Man Dies after Appendix Surgery.
https://www.perthnow.com.au/news/crime/young-man-dies-after-appendix-surgery-c-8360420
[9]  Oglesby, F., Ray, A., Shurlock, T., Mitra, T. and Cook, T. (2022) Litigation Related to Anaesthesia: Analysis of Claims against the NHS in England 2008-2018 and Comparison against Previous Claim Patterns. Anaesthesia, 77, 527-537.
https://doi.org/10.1111/anae.15685
[10]  Arnstein, F. (1997) Catalogue of Human Error. British Journal of Anaesthesia, 79, 645-656.
https://doi.org/10.1093/bja/79.5.645
[11]  Rayan, A.A., Hemdan, S.E. and Shetaia, A.M. (2019) Root Cause Analysis of Blunders in Anesthesia. Anesthesia. Essays and Researches, 13, 193-198.
https://doi.org/10.4103/aer.AER_47_19
[12]  Donaldson, M.S., Corrigan, J.M. and Kohn, L.T. (2000) To Err Is Human: Building a Safer Health System. National Academies Press, Washington DC.
[13]  Merhavy, Z., Merhavy, C. and Varkey, T. (2021) Anesthetic Drugs: A Comprehensive Overview for Anesthesiologists. Journal of Clinical Anesthesia and Intensive Care, 2, 42-53.
https://doi.org/10.46439/anesthesia.2.012
[14]  Choy, C.Y. (2008) Critical Incident Monitoring in Anaesthesia. Current Opinion in Anesthesiology, 21, 183-186.
https://doi.org/10.1097/ACO.0b013e3282f33592
[15]  Choy, Y. (2006) Critical Incident Monitoring in Anaesthesia. The Medical Journal of Malaysia, 61, 577-585.
[16]  Connor, C.W. (2019) Artificial Intelligence and Machine Learning in Anesthesiology. Anesthesiology, 131, 1346-1359.
https://doi.org/10.1097/ALN.0000000000002694
[17]  Ehrenfeld, J.M. and Rehman, M.A. (2011) Anesthesia Information Management Systems: A Review of Functionality and Installation Considerations. Journal of Clinical Monitoring and Computing, 25, 71-79.
https://doi.org/10.1007/s10877-010-9256-y
[18]  Koubaa, A., Boulila, W., Ghouti, L., Alzahem, A. and Latif, S. (2023) Exploring ChatGPT Capabilities and Limitations: A Critical Review of the NLP Game Changer. (Preprint)
https://doi.org/10.20944/preprints202303.0438.v1
[19]  Cascella, M., Montomoli, J., Bellini, V. and Bignami, E. (2023) Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. Journal of Medical Systems, 47, Article No. 33.
https://doi.org/10.1007/s10916-023-01925-4
[20]  Eysenbach, G. (2023) The Role of ChatGPT, Generative Language Models and Artificial Intelligence in Medical Education: A Conversation with ChatGPT and a Call for Papers. JMIR Medical Education, 9, e46885.
https://doi.org/10.2196/46885
[21]  Bellini, V., Carna, E.R., Russo, M., Di Vincenzo, F., Berghenti, M., Baciarello, M. and Bignami, E. (2022) Artificial Intelligence and Anesthesia: A Narrative Review. Annals of Translational Medicine, 10, Article No. 528.
https://doi.org/10.21037/atm-21-7031
[22]  Arora, A. (2020) Artificial Intelligence: A New Frontier for Anaesthesiology Training. British Journal of Anaesthesia, 125, e407-e408.
https://doi.org/10.1016/j.bja.2020.06.049
[23]  Liévin, V., Hother, C.E. and Winther, O. (2022) Can Large Language Models Reason about Medical Questions? (Preprint)
[24]  Zissos, A. and Strunin, L. (1985) Computers in Anaesthesia. Canadian Anaesthetists Society Journal, 32, 374-384.
https://doi.org/10.1007/BF03011342
[25]  Russell, D. (1998) Intravenous Anaesthesia: Manual Infusion Schemes versus TCI Systems. Anaesthesia, 53, 42-45.
https://doi.org/10.1111/j.1365-2044.1998.53s113.x
[26]  Atchabahian, A. and Hemmerling, T.M. (2014) Robotic Anesthesia: How Is It Going to Change Our Practice? Anesthesiology and Pain Medicine, 4, e16468.
https://doi.org/10.5812/aapm.16468
[27]  Hemmerling, T.M., Taddei, R., Wehbe, M., Morse, J., Cyr, S. and Zaouter, C. (2011) Robotic Anesthesia—A Vision For the Future of Anesthesia. Translational Medicine@ UniSa, 1, 1-20.
[28]  Wehbe, M., Arbeid, E., Cyr, S., Mathieu, P.A., Taddei, R., Morse, J. and Hemmerling, T.M. (2014) A Technical Description of a Novel Pharmacological Anesthesia Robot. Journal of Clinical Monitoring and Computing, 28, 27-34.
https://doi.org/10.1007/s10877-013-9451-8
[29]  Hemmerling, T.M. (2020) Robots Will Perform Anesthesia in the Near Future. Anesthesiology, 132, 219-220.
https://doi.org/10.1097/ALN.0000000000003088
[30]  Gambus, P.L. and Jaramillo, S. (2019) Machine Learning in Anaesthesia: Reactive, proactive… Predictive! British Journal of Anaesthesia, 123, 401-403.
https://doi.org/10.1016/j.bja.2019.07.009
[31]  Seger, C. and Cannesson, M. (2020) Recent Advances in the Technology of Anesthesia. F1000Research, 9, Article No. 375.
https://doi.org/10.12688/f1000research.24059.1
[32]  Dutta, A., Sethi, N., Puri, G.D., Sood, J., Choudhary, P.K., Jain, A.K., Panday, B.C. and Gupta, M. (2022) Automated Closed-Loop Propofol Anesthesia versus Desflurane Inhalation Anesthesia in Obese Patients Undergoing Bariatric Surgery: A Comparative Randomized Analysis of Recovery Profile. (Preprint)
https://doi.org/10.21203/rs.3.rs-1668189/v1
[33]  McKendrick, M., Yang, S. and McLeod, G. (2021) The Use of Artificial Intelligence and Robotics in Regional Anaesthesia. Anaesthesia, 76, 171-181.
https://doi.org/10.1111/anae.15274
[34]  Montomoli, J., Hilty, M.P. and Ince, C. (2022) Artificial Intelligence in Intensive Care: Moving Towards Clinical Decision Support Systems. Minerva Anestesiologica, 88, 1066-1072.
[35]  Pirracchio, R. (2022) The Past, the Present and the Future of Machine Learning and Artificial Intelligence in Anesthesia and Post Anesthesia Care Units (PACU). Minerva Anestesiologica, 88, 961-969.
[36]  Dumitru, M., Berghi, O.N., Taciuc, I.-A., Vrinceanu, D., Manole, F. and Costache, A. (2022) Could Artificial Intelligence Prevent Intraoperative Anaphylaxis? Reference Review and Proof of Concept. Medicina, 58, Article 1530.
https://doi.org/10.3390/medicina58111530
[37]  Zaouter, C., Joosten, A., Rinehart, J., Struys, M.M. and Hemmerling, T.M. (2020) Autonomous Systems in Anesthesia: Where Do We Stand in 2020? A Narrative Review. Anesthesia & Analgesia, 130, 1120-1132.
https://doi.org/10.1213/ANE.0000000000004646
[38]  Xu, C., Zhu, Y., Wu, L., Yu, H., Liu, J., Zhou, F., Xiong, Q., Wang, S., Cui, S. and Huang, X. (2022) Evaluating the Effect of an Artificial Intelligence System on the Anesthesia Quality Control during Gastrointestinal Endoscopy with Sedation: A Randomized Controlled Trial. BMC Anesthesiology, 22, Article No. 313.
https://doi.org/10.1186/s12871-022-01796-1
[39]  Wingert, T., Lee, C. and Cannesson, M. (2021) Machine Learning, Deep Learning, and Closed Loop Devices—Anesthesia Delivery. Anesthesiology Clinics, 39, 565-581.
https://doi.org/10.1016/j.anclin.2021.03.012
[40]  Naaz, S. and Asghar, A. (2022) Artificial Intelligence, Nano-Technology and Genomic Medicine: The Future of Anaesthesia. Journal of Anaesthesiology Clinical Pharmacology, 38, 11-17.
https://doi.org/10.4103/joacp.JOACP_139_20
[41]  Hemmerling, T., Arbeid, E., Wehbe, M., Cyr, S., Taddei, R. and Zaouter, C. (2013) Evaluation of a Novel Closed-Loop Total Intravenous Anaesthesia Drug Delivery System: A Randomized Controlled Trial. British Journal of Anaesthesia, 110, 1031-1039.
https://doi.org/10.1093/bja/aet001
[42]  West, N., Van Heusden, K., Görges, M., Brodie, S., Rollinson, A., Petersen, C.L., Dumont, G.A., Ansermino, J.M. and Merchant, R.N. (2018) Design and Evaluation of a Closed-Loop Anesthesia System with Robust Control and Safety System. Anesthesia & Analgesia, 127, 883-894.
https://doi.org/10.1213/ANE.0000000000002663
[43]  Goverdhan Dutt Puri, P.M., Jayant, A. and Singh, G. (2023) CLADS Closed Loop Anaesthesia Delivery System.
http://www.clads-iaads.com/index.php?PageID=1058
[44]  AlertWatchTM (2023) The Intelligent Monitoring Platform.
https://www.alertwatch.com/
[45]  Intuitive (2023) Da Vinci Surgical Systems.
https://www.intuitive.com/en-us/products-and-services/da-vinci/systems
[46]  Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G. and King, D. (2019) Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Medicine, 17, Article No. 195.
https://doi.org/10.1186/s12916-019-1426-2
[47]  Hashimoto, D.A., Witkowski, E., Gao, L., Meireles, O. and Rosman, G. (2020) Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications and Limitations. Anesthesiology, 132, 379-394.
https://doi.org/10.1097/ALN.0000000000002960
[48]  Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H.ü., Jin, J., Zhou, M.M. and Zhang, B. (2021) Artificial Intelligence and Machine Learning-Aided Drug Discovery in Central Nervous System Diseases: State-of-the-Arts and Future Directions. Medicinal Research Reviews, 41, 1427-1473.
https://doi.org/10.1002/med.21764
[49]  Farghali, H., Canová, N.K. and Arora, M. (2021) The Potential Applications of Artificial Intelligence in Drug Discovery and Development. Physiological Research, 70, S715-S722.
https://doi.org/10.33549//physiolres.934765
[50]  Parikh, R.B. and Helmchen, L.A. (2022) Paying for Artificial Intelligence in Medicine. NPJ Digital Medicine, 5, Article No. 63.
https://doi.org/10.1038/s41746-022-00609-6
[51]  Singh, M. and Nath, G. (2022) Artificial Intelligence and Anesthesia: A Narrative Review. Saudi Journal of Anaesthesia, 16, 86-93.
https://doi.org/10.4103/sja.sja_669_21
[52]  Gambus, P. and Shafer, S.L. (2018) Artificial Intelligence for Everyone. Anesthesiology, 128, 431-433.
https://doi.org/10.1097/ALN.0000000000001984
[53]  Bellini, V., Valente, M., Gaddi, A.V., Pelosi, P. and Bignami E. (2022) Artificial Intelligence and Telemedicine in Anesthesia: Potential and Problems. Minerva Anestesiologica, 88, 729-734.
[54]  Holzinger, A., Langs, G., Denk, H., Zatloukal, K. and Müller, H. (2019) Causability and Explainability of Artificial Intelligence in Medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9, e1312.
https://doi.org/10.1002/widm.1312
[55]  Char, D.S. and Burgart, A. (2020) Machine Learning Implementation in Clinical Anesthesia: Opportunities and Challenges. Anesthesia and Analgesia, 130, 1709-1712.
https://doi.org/10.1213/ANE.0000000000004656
[56]  Benjamens, S., Dhunnoo, P. and Meskó, B. (2020) The State of Artificial Intelligence-Based FDA-Approved Medical Devices and Algorithms: An Online Database. NPJ Digital Medicine, 3, Article No. 118.
https://doi.org/10.1038/s41746-020-00324-0
[57]  Melvin, R.L., Broyles, M.G., Duggan, E.W., John, S., Smith, A.D. and Berkowitz, D.E. (2022) Artificial Intelligence in Perioperative Medicine: A Proposed Common Language with Applications to FDA-Approved Devices. Frontiers in Digital Health, 4, Article 872675.
https://doi.org/10.3389/fdgth.2022.872675
[58]  Nair, B.G., Gabel, E., Hofer, I., Schwid, H.A. and Cannesson, M. (2017) Intraoperative Clinical Decision Support for Anesthesia: A Narrative Review of Available Systems. Anesthesia & Analgesia, 124, 603-617.
https://doi.org/10.1213/ANE.0000000000001636
[59]  Barto, A.G. and Sutton, R.S. (1997) Reinforcement Learning in Artificial Intelligence. Advances in Psychology, 121, 358-386.
https://doi.org/10.1016/S0166-4115(97)80105-7
[60]  Vlamou, E. and Papadopoulos, B. (2019) Fuzzy Logic Systems and Medical Applications. AIMS Neuroscience, 6, 266-272.
https://doi.org/10.3934/Neuroscience.2019.4.266
[61]  Ingle, S. and Phute, M. (2016) Tesla Autopilot: Semi Autonomous Driving, an Uptick for Future Autonomy. International Research Journal of Engineering and Technology, 3, 369-372.
[62]  Zwakman, D.S., Pal, D. and Arpnikanondt, C. (2021) Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa. SN Computer Science, 2, Article No. 28.
https://doi.org/10.1007/s42979-020-00424-4
[63]  Lee, J.Y. (2023) Can an Artificial Intelligence Chatbot Be the Author of a Scholarly Article? Journal of Educational Evaluation for Health Professions, 20, Article No. 6.
[64]  Transformer, C.G.P.-T. and Zhavoronkov, A. (2022) Rapamycin in the Context of Pascal’s Wager: Generative Pre-Trained Transformer Perspective. Oncoscience, 9, 82-84.
https://doi.org/10.18632/oncoscience.571
[65]  Bhattacharya, K., Bhattacharya, A.S., Bhattacharya, N., Yagnik, V.D., Garg, P. and Kumar, S. (2023) ChatGPT in Surgical Practice—A New Kid on the Block. Indian Journal of Surgery.
https://doi.org/10.1007/s12262-023-03727-x
[66]  Vasey, B., Nagendran, M., Campbell, B., Clifton, D.A., Collins, G.S., Denaxas, S., Denniston, A.K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) Reporting Guideline for the Early-Stage Clinical Evaluation of Decision Support Systems Driven by Artificial Intelligence: DECIDE-AI. Nature Medicine, 28, 924-933.
https://doi.org/10.1038/s41591-022-01772-9
[67]  Liu, N., Chazot, T., Hamada, S., Landais, A., Boichut, N., Dussaussoy, C., Trillat, B., Beydon, L., Samain, E., Sessler, D.I. and Marc, F. (2011) Closed-Loop Coadministration of Propofol and Remifentanil Guided by Bispectral Index: A Randomized Multicenter Study. Anesthesia & Analgesia, 112, 546-557.
https://doi.org/10.1213/ANE.0b013e318205680b
[68]  Haro-Mendoza, D., Pérez-Escamirosa, F., Pineda-Martínez, D. and Gonzalez-Villela, V.J. (2022) Needle Path Planning in Semiautonomous and Teleoperated Robot-Assisted Epidural Anaesthesia Procedure: A Proof of Concept. The International Journal of Medical Robotics and Computer Assisted Surgery, 18, e2434.
https://doi.org/10.1002/rcs.2434
[69]  Moon, J.S. and Cannesson, M. (2022) A Century of Technology in Anesthesia & Analgesia. Anesthesia & Analgesia, 135, S48-S61.
https://doi.org/10.1213/ANE.0000000000005986
[70]  Alser, M. and Waisberg, E. (2023) Concerns with the Usage of ChatGPT in Academia and Medicine: A Viewpoint. American Journal of Medicine Open, 9, Article ID: 100036.
https://doi.org/10.1016/j.ajmo.2023.100036
[71]  Johnson, D., Goodman, R., Patrinely, J., Stone, C., Zimmerman, E., Donald, R., Chang, S., Berkowitz, S., Finn, A. and Jahangir, E. (2023) Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model. (Preprint)
https://doi.org/10.21203/rs.3.rs-2566942/v1
[72]  Kung, T.H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G. and Maningo, J. (2023) Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education Using Large Language Models. PLOS Digital Health, 2, e0000198.
https://doi.org/10.1371/journal.pdig.0000198
[73]  Huh, S. (2023) Are ChatGPT’s Knowledge and Interpretation Ability Comparable to Those of Medical Students in Korea for Taking a Parasitology Examination?: A Descriptive Study. Journal of Educational Evaluation for Health Professions, 20, Article No. 1.
[74]  Hemmerling, T. and Giacalone, M. (2016) An Introduction to Robots in Anaesthesia. ICU Management & Practice, 16, 96-100.
[75]  Tremper, K.K., Mace, J.J., Gombert, J.M., Tremper, T.T., Adams, J.F. and Bagian, J.P. (2018) Design of a Novel Multifunction Decision Support Display for Anesthesia Care: AlertWatch® OR. BMC Anesthesiology, 18, Article No. 16.
https://doi.org/10.1186/s12871-018-0478-8
[76]  Sathishkumar, S., Lai, M., Picton, P., Kheterpal, S., Morris, M., Shanks, A. and Ramachandran, S.K. (2015) Behavioral Modification of Intraoperative Hyperglycemia Management with a Novel Real-Time Audiovisual Monitor. Anesthesiology, 123, 29-37.
https://doi.org/10.1097/ALN.0000000000000699
[77]  Jones, J.H., Nittur, V.R., Fleming, N. and Applegate, R.L. (2021) Simultaneous Comparison of Depth of Sedation Performance between SedLine and BIS during General Anesthesia Using Custom Passive Interface Hardware: Study Protocol for a Prospective, Non-Blinded, Non-Randomized Trial. BMC Anesthesiology, 21, Article No. 105.
https://doi.org/10.1186/s12871-021-01326-5
[78]  Masimo® (2023) Next Generation SedLine(R) Brain Function Monitoring.
https://www.masimo.com/products/continuous/root/root-sedline/
[79]  Pasin, L., Nardelli, P., Pintaudi, M., Greco, M., Zambon, M., Cabrini, L. and Zangrillo, A. (2017) Closed-Loop Delivery Systems Versus Manually Controlled Administration of Total IV Anesthesia: A Meta-Analysis of Randomized Clinical Trials. Anesthesia & Analgesia, 124, 456-464.
https://doi.org/10.1213/ANE.0000000000001394
[80]  Hemmerling, T., Arbeid, E., Wehbe, M., Cyr, S., Giunta, F. and Zaouter, C. (2013) Transcontinental Anaesthesia: A Pilot Study. British Journal of Anaesthesia, 110, 758-763.
https://doi.org/10.1093/bja/aes498
[81]  Zaouter, C., Hemmerling, T.M., Lanchon, R., Valoti, E., Remy, A., Leuillet, S. and Ouattara, A. (2016) The Feasibility of a Completely Automated Total IV Anesthesia Drug Delivery System for Cardiac Surgery. Anesthesia & Analgesia, 123, 885-893.
https://doi.org/10.1213/ANE.0000000000001152
[82]  Goudra, B., Singh, P.M. and Lichtenstein, G.R. (2020) Medical, Political and Economic Considerations for the Use of MAC for Endoscopic Sedation: Big Price, Little Justification? Digestive Diseases and Sciences, 65, 2466-2472.
https://doi.org/10.1007/s10620-020-06464-3
[83]  Alexander, J.C. and Joshi, G.P. (2018) Anesthesiology, Automation and Artificial Intelligence. Baylor University Medical Center Proceedings, 31, 117-119.
https://doi.org/10.1080/08998280.2017.1391036
[84]  Goudra, B. and Singh, P.M. (2017) Failure of Sedasys: Destiny or Poor Design? Anesthesia & Analgesia, 124, 686-688.
https://doi.org/10.1213/ANE.0000000000001643
[85]  Puri, G., Kumar, B. and Aveek, J. (2007) Closed-Loop Anaesthesia Delivery System (CLADSTM) Using Bispectral Index: A Performance Assessment Study. Anaesthesia and Intensive Care, 35, 357-362.
https://doi.org/10.1177/0310057X0703500306
[86]  Puri, G.D., Mathew, P.J., Biswas, I., Dutta, A., Sood, J., Gombar, S., Palta, S., Tsering, M., Gautam, P. and Jayant, A. (2016) A Multicenter Evaluation of a Closed-Loop Anesthesia Delivery System: A Randomized Controlled Trial. Anesthesia & Analgesia, 122, 106-114.
https://doi.org/10.1213/ANE.0000000000000769
[87]  De Smet, T., Struys, M.M., Neckebroek, M.M., Van den Hauwe, K., Bonte, S. and Mortier, E.P. (2008) The Accuracy and Clinical Feasibility of a New Bayesian-Based Closed-Loop Control System for Propofol Administration Using the Bispectral Index as a Controlled Variable. Anesthesia & Analgesia, 107, 1200-1210.
https://doi.org/10.1213/ane.0b013e31817bd1a6
[88]  Liu, Y., Li, M., Yang, D., Zhang, X., Wu, A., Yao, S., Xue, Z. and Yue, Y. (2015) Closed-Loop Control Better than Open-Loop Control of Profofol TCI Guided by BIS: A Randomized, Controlled, Multicenter Clinical Trial to Evaluate the CONCERT-CL Closed-Loop System. PLOS ONE, 10, e0123862.
https://doi.org/10.1371/journal.pone.0123862
[89]  Liu, N., Chazot, T., Genty, A., Landais, A., Restoux, A., McGee, K., Laloë, P.-A., Trillat, B., Barvais, L. and Fischler, M. (2006) Titration of Propofol for Anesthetic Induction and Maintenance Guided by the Bispectral Index: Closed-Loop versus Manual Control: A Prospective, Randomized, Multicenter Study. The Journal of the American Society of Anesthesiologists, 104, 686-695.
https://doi.org/10.1097/00000542-200604000-00012
[90]  Morley, A., Derrick, J., Mainland, P., Lee, B. and Short, T. (2000) Closed Loop Control of Anaesthesia: An Assessment of the Bispectral Index as the Target of Control. Anaesthesia, 55, 953-959.
https://doi.org/10.1046/j.1365-2044.2000.01527.x
[91]  Tighe, P.J., Badiyan, S., Luria, I., Lampotang, S. and Parekattil, S. (2010) Robot-Assisted Airway Support: A Simulated Case. Anesthesia and Analgesia, 111, 929-931.
https://doi.org/10.1213/ANE.0b013e3181ef73ec
[92]  Hemmerling, T.M., Wehbe, M., Zaouter, C., Taddei, R. and Morse, J. (2012) The Kepler Intubation System. Anesthesia & Analgesia, 114, 590-594.
https://doi.org/10.1213/ANE.0b013e3182410cbf
[93]  Morse, J., Terrasini, N., Wehbe, M., Philippona, C., Zaouter, C., Cyr, S. and Hemmerling, T. (2014) Comparison of Success Rates, Learning Curves, and Inter-Subject Performance Variability of Robot-Assisted and Manual Ultrasound-Guided Nerve Block Needle Guidance in Simulation. British Journal of Anaesthesia, 112, 1092-1097.
https://doi.org/10.1093/bja/aet440
[94]  Ng, Z.Q., Jung, J.K. and Theophilus, M. (2021) Artificial Intelligence in Pre-Operative Assessment of Patients in Colorectal Surgery. Turkish Journal of Colorectal Disease, 31, 99-104.
https://doi.org/10.4274/tjcd.galenos.2021.2021-2-6
[95]  Lin, C.-S., Li, Y.-C., Mok, M.S., Wu, C.-C., Chiu, H.-W. and Lin, Y.-H. (2002) Neural Network Modeling to Predict the Hypnotic Effect of Propofol Bolus Induction. Proceedings of the AMIA Symposium, San Antonio, TX, 9-13 November 2002, 450-453.
[96]  Hemmerling, T.M., Taddei, R., Wehbe, M., Cyr, S., Zaouter, C. and Morse, J. (2013) First Robotic Ultrasound-Guided Nerve Blocks in Humans Using the Magellan System. Anesthesia & Analgesia, 116, 491-494.
https://doi.org/10.1213/ANE.0b013e3182713b49
[97]  Smistad, E. and Løvstakken, L. (2016) Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks. In: Carneiro, G., et al., Eds., DLMIA 2016, LABELS 2016: Deep Learning and Data Labeling for Medical Applications, Lecture Notes in Computer Science, Vol. 10008, Springer, Cham, 30-38.
https://doi.org/10.1007/978-3-319-46976-8_4
[98]  Atee, M., Hoti, K. and Hughes, J.D. (2018) A Technical Note on the PainChek™ System: A Web Portal and Mobile Medical Device for Assessing Pain in People with Dementia. Frontiers in Aging Neuroscience, 10, Article 117.
https://doi.org/10.3389/fnagi.2018.00117
[99]  Gram, M., Erlenwein, J., Petzke, F., Falla, D., Przemeck, M., Emons, M.I., Reuster, M., Olesen, S. and Drewes, A.M. (2017) Prediction of Postoperative Opioid Analgesia Using Clinical-Experimental Parameters and Electroencephalography. European Journal of Pain, 21, 264-277.
https://doi.org/10.1002/ejp.921
[100]  Benzy, V. and Jasmin, E. (2015) A Combined Wavelet and Neural Network Based Model for Classifying Depth of Anaesthesia. Procedia Computer Science, 46, 1610-1617.
https://doi.org/10.1016/j.procs.2015.02.093
[101]  Combes, C., Meskens, N., Rivat, C. and Vandamme, J.-P. (2008) Using a KDD Process to Forecast the Duration of Surgery. International Journal of Production Economics, 112, 279-293.
https://doi.org/10.1016/j.ijpe.2006.12.068
[102]  Devi, S.P., Rao, K.S. and Sangeetha, S.S. (2012) Prediction of Surgery Times and Scheduling of Operation Theaters in Optholmology Department. Journal of Medical Systems, 36, 415-430.
https://doi.org/10.1007/s10916-010-9486-z
[103]  Sahai, A., Wong, J.C., Gould, M. and Byrne, M.F. (2013) Tu1380 Multicenter Preliminary Experience with the SEDASYS Propofol Infusion Pump for Colonoscopy in Routine Clinical Practice: Safety and Endoscopist Satisfaction. Gastrointestinal Endoscopy, 77, AB520.
https://doi.org/10.1016/j.gie.2013.03.861
[104]  Abel, J.H., Badgeley, M.A., Meschede-Krasa, B., Schamberg, G., Garwood, I.C., Lecamwasam, K., Chakravarty, S., Zhou, D.W., Keating, M. and Purdon, P.L. (2021) Machine Learning of EEG Spectra Classifies Unconsciousness during GABAergic Anesthesia. PLOS ONE, 16, e0246165.
https://doi.org/10.1371/journal.pone.0246165
[105]  Birch, J., Creel, K.A., Jha, A.K. and Plutynski, A. (2022) Clinical Decisions Using AI must Consider Patient Values. Nature Medicine, 28, 229-232.
https://doi.org/10.1038/s41591-021-01624-y
[106]  Mathur, S., Patel, J., Goldstein, S. and Jain, A. (2021) Bispectral Index. StatPearls Publishing LLC, Tampa, FL.
[107]  Helmreich, R.L. (2000) On Error Management: Lessons from Aviation. BMJ, 320, 781-785.
https://doi.org/10.1136/bmj.320.7237.781
[108]  Lee, H.-C., Ryu, H.-G., Park, Y., Yoon, S.B., Yang, S.M., Oh, H.-W. and Jung, C.-W. (2019) Data Driven Investigation of Bispectral Index Algorithm. Scientific Reports, 9, Article No. 13769.
https://doi.org/10.1038/s41598-019-50391-x
[109]  Shen, M.W. (2022) Trust in AI: Interpretability Is Not Necessary or Sufficient, While Black-Box Interaction Is Necessary and Sufficient. (Preprint)

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