Revolutionizing Post Anesthesia Care Unit with Artificial Intelligence: A Narrative Review
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Abstract
Artificial intelligence (AI) is increasingly being utilized in Post-Anesthesia Care Units (PACUs) to improve patient monitoring and care. This narrative review explores the current use of AI in PACUs and discusses the potential benefits and challenges associated with its implementation and highlights how AI technologies such as predictive analytics, machine learning algorithms, and robotics can enhance patient safety, reduce human error, and improve outcomes in the PACU setting. Overall, this narrative review provides insights into the evolving role of AI in PACUs and offers recommendations for future research and practice in this area.
[1] Solanki SL, Pandrowala S, Nayak A, Bhandare M, Ambulkar RP, Shrikhande SV. Artificial intelligence in peri-operative management of major gastrointestinal surgeries. World J Gastroenterol. 2021; 27(21):2758-70.
[2] Li YY, Wang JJ, Huang SH, Kuo CL, Chen JY, Liu CF, et al. Implementation of a machine learning application in preoperative risk assessment for hip repair surgery. BMC anesthesiol. 2022; 22(1):116.
[3] Meiring C, Dixit A, Harris S, MacCallum NS, Brealey DA, Watkinson PJ, et al. Optimal intensive care outcome prediction over time using machine learning. PloS one. 2018; 13(11):e0206862.
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[6] Bishara A, Wong A, Wang L, Chopra M, Fan W, Lin A, et al. Opal: an implementation science tool for machine learning clinical decision support in anesthesia. J Clin Monit Comput. 2022:1-11.
[7] Wilson Jr JP, Kumbhare D, Kandregula S, Oderhowho A, Guthikonda B, Hoang S. Proposed Applications of Machine Learning to Intraoperative Neuromonitoring during Spine Surgeries. Neurosci Informatics. 2023:100143.
[8] Wingert T, Lee C, Cannesson M. Machine learning, deep learning, and closed loop devices—anesthesia delivery. Anesth Clin. 2021; 39(3):565-81.
[9] Kang AR, Lee J, Jung W, Lee M, Park SY, Woo J, et al. Development of a prediction model for hypotension after induction of anesthesia using machine learning. PloS one. 2020; 15(4):e0231172.
[10] Tacke M, Kochs EF, Mueller M, Kramer S, Jordan D, Schneider G. Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia. Plos one. 2020; 15(8):e0238249.
[11] Karpagavalli S, Jamuna K, Vijaya M. Machine learning approach for preoperative anaesthetic risk prediction. Int J Recent Trends Eng. 2009; 1(2):19.
[12] Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020; 92(4):807-12.
[13] Rheingold H. Tools for thought: The history and future of mind-expanding technology. DLC. 1995.
[14] Cooper SB. From descartes to Turing: the computational content of supervenience. Information And Computation: Essays on Scientific and Philosophical Understanding of Foundations of Information and Computation. World Sci. 2011; p. 107-48.
[15] McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biol. 1990; 52:99-115.
[16] Mendell H. Aristotle and mathematics. Stanford Encyclopedia Philos. 2004.
[17] Newell A. Introduction to the COMTEX Microfiche Edition of Reports on Artificial Intelligence from Carnegie-Mellon University. AI Magazine. 1984; 5(3):35-.
[18] Cooper SB, Van Leeuwen J. Alan Turing: His work and impact. Elsevier; 2013.
[19] Newell A. Intellectual issues in the history of artificial intelligence. Artificial Intelligence: Critical Concepts. Routledge. 1982:25-70.
[20] Benko A, Lányi CS. History of artificial intelligence. Encyclopedia of Information Science and Technology, Second Edition. IGI global. 2009; 1759-62.
[21] El Naqa I, Murphy MJ. What is machine learning?: Springer. 2015.
[22] Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med. 2001; 23(1):89-109.
[23] Sammut C, Webb GI. Encyclopedia of machine learning. Springer. 2011.
[24] Morales EF, Escalante HJ. A brief introduction to supervised, unsupervised, and reinforcement learning. Biosignal processing and classification using computational learning and intelligence. Elsevier. 2022; 111-29.
[25] Dhanaraj RK, Rajkumar K, Hariharan U. Enterprise IoT modeling: supervised, unsupervised, and reinforcement learning. Bus Intell Enterp IoT. 2020; 55-79.
[26] Hady MFA, Schwenker F. Semi-supervised learning. Handbook on Neural Information Processing. Springer. 2013; 215-39.
[27] Settles B, editor From theories to queries: Active learning in practice. Active learning and experimental design workshop in conjunction with AISTATS 2010. JMLR W&CP. 2011.
[28] Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. J Big Data. 2016; 3:1-40.
[29] Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology. 2020; 132(2):379-94.
[30] Connor CW. Artificial intelligence and machine learning in anesthesiology. Anesthesiology. 2019; 131(6):1346-59.
[31] Brennan M, Hagan JD, Giordano C, Loftus TJ, Price CE, Aytug H, Tighe PJ. Multiobjective optimization challenges in peri-operative anesthesia: A review. Surgery. 2021; 170(1):320-4.
[32] Hashemi S, Yousefzadeh Z, Abin AA, Ejmalian A, Nabavi S, Dabbagh A. Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications. J Cell Mol Anesth. 2024; 9(1).
[33] Alexander JC, Romito BT, Çobanoglu MC. The present and future role of artificial intelligence and machine learning in anesthesiology. Int Anesthesiol Clin. 2020; 58(4):7-16.
[34] Char DS, Burgart A. Machine-learning implementation in clinical anesthesia: opportunities and challenges. Anesth Analg. 2020; 130(6):1709-12.
[35] Gabriel RA, Harjai B, Simpson S, Goldhaber N, Curran BP, Waterman RS. Machine learning-based models predicting outpatient surgery end time and recovery room discharge at an ambulatory surgery center. Anesth Analg. 2022; 135(1):159-69.
[36] Chae D. Data science and machine learning in anesthesiology. Korean J Anesthesiol. 2020; 73(4):285.
[37] Thiele RH, Rea KM, Turrentine FE, Friel CM, Hassinger TE, Goudreau BJ, et al. Standardization of care: impact of an enhanced recovery protocol on length of stay, complications, and direct costs after colorectal surgery. J Am Coll Surg. 2015; 220(4):430-43.
[38] Stark PA, Myles PS, Burke JA. Development and psychometric evaluation of a post-operative quality of recovery score: the QoR-15. Anesthesiology. 2013; 118(6):1332-40.
[39] Kim W, Kil H, Kang JW, Park H. Prediction on lengths of stay in the Postanesthesia Care Unit following general anesthesia: Preliminary study of the neural network and logistic regression modelling. J Korean Med Sci. 2000; 15:25-30.
[40] Huang X, Tan R, Lin JW, Li G, Xie J. Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study. BMC Anesthesiol. 2023; 23(1):83.
[41] Peng R, Saghafi F, Maxwell H. Discharge delay from the post anaesthesia care unit: a nursing perspective. J Perioper Nurs. 2023; 36(2):1.
[42] Kol Y, Filhaver A, Shitrit S, Rubin L. Determining the effective length of stay for post-operative patients in the PACU through the location of influencing factors. Br J Anaesth Recover Nurs. 2009; 10(3):51-6.
[43] Yang S, Li H, Lin Z, Song Y, Lin C, Zhou T. Quantitative Analysis of Anesthesia Recovery Time by Machine Learning Prediction Models. Mathematics. 2022; 10(15):2772.
[44] Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, et al. Feature selection: A data perspective. ACM Comput Surv. 2017; 50(6):1-45.
[45] Penprase B, Brunetto E, Dahmani E, Forthoffer JJ, Kapoor S. The efficacy of preemptive analgesia for post-operative pain control: a systematic review of the literature. AORN J. 2015; 101(1):94-105. e8.
[46] Halliwell R. Patient-controlled analgesia. Acute Pain Management. Scientific Evidence. 2020.
[47] Palmer PP, Miller RD. Current and developing methods of patient-controlled analgesia. Anesth Clin. 2010; 28(4):587-99.
[48] Yang SF, Ku TH, Jeng AAK, Jan RH, Tseng YC, Wang KC, et al. iPCA: an integration information system for patient controlled analgesia using wireless techniques. Int J Ad Hoc Ubiquitous Comput. 2013; 13(1):48-58.
[49] Wang R, Wang S, Duan N, Wang Q. From patient-controlled analgesia to artificial intelligence-assisted patient-controlled analgesia: practices and perspectives. Front Med. 2020; 7:145.
[50] Atee M, Hoti K, Hughes JD. A technical note on the PainChek™ system: a web portal and mobile medical device for assessing pain in people with dementia. Front Aging Neurosci. 2018; 10:117.
[51] Gram M, Erlenwein J, Petzke F, Falla D, Przemeck M, Emons MI, et al. Prediction of post-operative opioid analgesia using clinical‐experimental parameters and electroencephalography. Eur J Pain. 2017; 21(2):264-77.
[52] Bowness JS, Burckett-St Laurent D, Hernandez N, Keane PA, Lobo C, Margetts S, et al. Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study. Br J Anaesth. 2023; 130(2):217-25.
[53] Bowness JS, Metcalfe D, El-Boghdadly K, Thurley N, Morecroft M, Hartley T, et al. Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines. Br J Anaesth. 2024.
[54] Ermer SC, Farney RJ, Johnson KB, Orr JA, Egan TD, Brewer LM. An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing. Anesth Analg. 2020; 130(5):1147-56.
[55] Rudolph JL, Marcantonio ER. Post-operative delirium: acute change with long-term implications. Anesth Analg. 2011; 112(5):1202-11.
[56] Liu Y, Shen W, Tian Z. Using machine learning algorithms to predict high-risk factors for post-operative delirium in elderly patients. Clin Interv Aging. 2023:157-68.
[57] Bishara A, Chiu C, Whitlock EL, Douglas VC, Lee S, Butte AJ, et al. Post-operative delirium prediction using machine learning models and preoperative electronic health record data. BMC anesthesiol. 2022; 22:1-12.
[58] Hackett NJ, De Oliveira GS, Jain UK, Kim JY. ASA class is a reliable independent predictor of medical complications and mortality following surgery. Int J Surg. 2015; 18:184-90.
[59] Chelazzi C, Villa G, Manno A, Ranfagni V, Gemmi E, Romagnoli S. The new SUMPOT to predict post-operative complications using an Artificial Neural Network. Sci Rep. 2021; 11(1):22692.
[60] Traeger L, Bedrikovetski S, Hanna JE, Moore JW, Sammour T. Machine learning prediction model for post-operative ileus following colorectal surgery. ANZ J Surg. 2024.
[61] Zhou CM, Li H, Xue Q, Yang JJ, Zhu Y. Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery. Heliyon. 2024.
[62] Liem VG, Hoeks SE, Mol KH, Potters JW, Grüne F, Stolker RJ, et al. Post-operative hypotension after noncardiac surgery and the association with myocardial injury. Anesthesiology. 2020; 133(3):510-22.
[63] Tol JTM, Terwindt LE, Rellum SR, Wijnberge M, van der Ster BJP, Kho E, et al. Performance of a Machine Learning Algorithm to Predict Hypotension in Spontaneously Breathing Non-Ventilated Post-Anesthesia and ICU Patients. J Pers Med. 2024; 14(2):210.
[2] Li YY, Wang JJ, Huang SH, Kuo CL, Chen JY, Liu CF, et al. Implementation of a machine learning application in preoperative risk assessment for hip repair surgery. BMC anesthesiol. 2022; 22(1):116.
[3] Meiring C, Dixit A, Harris S, MacCallum NS, Brealey DA, Watkinson PJ, et al. Optimal intensive care outcome prediction over time using machine learning. PloS one. 2018; 13(11):e0206862.
[4] Rojas JC, Carey KA, Edelson DP, Venable LR, Howell MD, Churpek MM. Predicting intensive care unit readmission with machine learning using electronic health record data. Ann Am Thorac Soc. 2018; 15(7):846-53.
[5] Hyland SL, Faltys M, Hüser M, Lyu X, Gumbsch T, Esteban C, et al. Early prediction of circulatory failure in the intensive care unit using machine learning. Nat Med. 2020; 26(3):364-73.
[6] Bishara A, Wong A, Wang L, Chopra M, Fan W, Lin A, et al. Opal: an implementation science tool for machine learning clinical decision support in anesthesia. J Clin Monit Comput. 2022:1-11.
[7] Wilson Jr JP, Kumbhare D, Kandregula S, Oderhowho A, Guthikonda B, Hoang S. Proposed Applications of Machine Learning to Intraoperative Neuromonitoring during Spine Surgeries. Neurosci Informatics. 2023:100143.
[8] Wingert T, Lee C, Cannesson M. Machine learning, deep learning, and closed loop devices—anesthesia delivery. Anesth Clin. 2021; 39(3):565-81.
[9] Kang AR, Lee J, Jung W, Lee M, Park SY, Woo J, et al. Development of a prediction model for hypotension after induction of anesthesia using machine learning. PloS one. 2020; 15(4):e0231172.
[10] Tacke M, Kochs EF, Mueller M, Kramer S, Jordan D, Schneider G. Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia. Plos one. 2020; 15(8):e0238249.
[11] Karpagavalli S, Jamuna K, Vijaya M. Machine learning approach for preoperative anaesthetic risk prediction. Int J Recent Trends Eng. 2009; 1(2):19.
[12] Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020; 92(4):807-12.
[13] Rheingold H. Tools for thought: The history and future of mind-expanding technology. DLC. 1995.
[14] Cooper SB. From descartes to Turing: the computational content of supervenience. Information And Computation: Essays on Scientific and Philosophical Understanding of Foundations of Information and Computation. World Sci. 2011; p. 107-48.
[15] McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biol. 1990; 52:99-115.
[16] Mendell H. Aristotle and mathematics. Stanford Encyclopedia Philos. 2004.
[17] Newell A. Introduction to the COMTEX Microfiche Edition of Reports on Artificial Intelligence from Carnegie-Mellon University. AI Magazine. 1984; 5(3):35-.
[18] Cooper SB, Van Leeuwen J. Alan Turing: His work and impact. Elsevier; 2013.
[19] Newell A. Intellectual issues in the history of artificial intelligence. Artificial Intelligence: Critical Concepts. Routledge. 1982:25-70.
[20] Benko A, Lányi CS. History of artificial intelligence. Encyclopedia of Information Science and Technology, Second Edition. IGI global. 2009; 1759-62.
[21] El Naqa I, Murphy MJ. What is machine learning?: Springer. 2015.
[22] Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med. 2001; 23(1):89-109.
[23] Sammut C, Webb GI. Encyclopedia of machine learning. Springer. 2011.
[24] Morales EF, Escalante HJ. A brief introduction to supervised, unsupervised, and reinforcement learning. Biosignal processing and classification using computational learning and intelligence. Elsevier. 2022; 111-29.
[25] Dhanaraj RK, Rajkumar K, Hariharan U. Enterprise IoT modeling: supervised, unsupervised, and reinforcement learning. Bus Intell Enterp IoT. 2020; 55-79.
[26] Hady MFA, Schwenker F. Semi-supervised learning. Handbook on Neural Information Processing. Springer. 2013; 215-39.
[27] Settles B, editor From theories to queries: Active learning in practice. Active learning and experimental design workshop in conjunction with AISTATS 2010. JMLR W&CP. 2011.
[28] Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. J Big Data. 2016; 3:1-40.
[29] Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology. 2020; 132(2):379-94.
[30] Connor CW. Artificial intelligence and machine learning in anesthesiology. Anesthesiology. 2019; 131(6):1346-59.
[31] Brennan M, Hagan JD, Giordano C, Loftus TJ, Price CE, Aytug H, Tighe PJ. Multiobjective optimization challenges in peri-operative anesthesia: A review. Surgery. 2021; 170(1):320-4.
[32] Hashemi S, Yousefzadeh Z, Abin AA, Ejmalian A, Nabavi S, Dabbagh A. Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications. J Cell Mol Anesth. 2024; 9(1).
[33] Alexander JC, Romito BT, Çobanoglu MC. The present and future role of artificial intelligence and machine learning in anesthesiology. Int Anesthesiol Clin. 2020; 58(4):7-16.
[34] Char DS, Burgart A. Machine-learning implementation in clinical anesthesia: opportunities and challenges. Anesth Analg. 2020; 130(6):1709-12.
[35] Gabriel RA, Harjai B, Simpson S, Goldhaber N, Curran BP, Waterman RS. Machine learning-based models predicting outpatient surgery end time and recovery room discharge at an ambulatory surgery center. Anesth Analg. 2022; 135(1):159-69.
[36] Chae D. Data science and machine learning in anesthesiology. Korean J Anesthesiol. 2020; 73(4):285.
[37] Thiele RH, Rea KM, Turrentine FE, Friel CM, Hassinger TE, Goudreau BJ, et al. Standardization of care: impact of an enhanced recovery protocol on length of stay, complications, and direct costs after colorectal surgery. J Am Coll Surg. 2015; 220(4):430-43.
[38] Stark PA, Myles PS, Burke JA. Development and psychometric evaluation of a post-operative quality of recovery score: the QoR-15. Anesthesiology. 2013; 118(6):1332-40.
[39] Kim W, Kil H, Kang JW, Park H. Prediction on lengths of stay in the Postanesthesia Care Unit following general anesthesia: Preliminary study of the neural network and logistic regression modelling. J Korean Med Sci. 2000; 15:25-30.
[40] Huang X, Tan R, Lin JW, Li G, Xie J. Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study. BMC Anesthesiol. 2023; 23(1):83.
[41] Peng R, Saghafi F, Maxwell H. Discharge delay from the post anaesthesia care unit: a nursing perspective. J Perioper Nurs. 2023; 36(2):1.
[42] Kol Y, Filhaver A, Shitrit S, Rubin L. Determining the effective length of stay for post-operative patients in the PACU through the location of influencing factors. Br J Anaesth Recover Nurs. 2009; 10(3):51-6.
[43] Yang S, Li H, Lin Z, Song Y, Lin C, Zhou T. Quantitative Analysis of Anesthesia Recovery Time by Machine Learning Prediction Models. Mathematics. 2022; 10(15):2772.
[44] Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, et al. Feature selection: A data perspective. ACM Comput Surv. 2017; 50(6):1-45.
[45] Penprase B, Brunetto E, Dahmani E, Forthoffer JJ, Kapoor S. The efficacy of preemptive analgesia for post-operative pain control: a systematic review of the literature. AORN J. 2015; 101(1):94-105. e8.
[46] Halliwell R. Patient-controlled analgesia. Acute Pain Management. Scientific Evidence. 2020.
[47] Palmer PP, Miller RD. Current and developing methods of patient-controlled analgesia. Anesth Clin. 2010; 28(4):587-99.
[48] Yang SF, Ku TH, Jeng AAK, Jan RH, Tseng YC, Wang KC, et al. iPCA: an integration information system for patient controlled analgesia using wireless techniques. Int J Ad Hoc Ubiquitous Comput. 2013; 13(1):48-58.
[49] Wang R, Wang S, Duan N, Wang Q. From patient-controlled analgesia to artificial intelligence-assisted patient-controlled analgesia: practices and perspectives. Front Med. 2020; 7:145.
[50] Atee M, Hoti K, Hughes JD. A technical note on the PainChek™ system: a web portal and mobile medical device for assessing pain in people with dementia. Front Aging Neurosci. 2018; 10:117.
[51] Gram M, Erlenwein J, Petzke F, Falla D, Przemeck M, Emons MI, et al. Prediction of post-operative opioid analgesia using clinical‐experimental parameters and electroencephalography. Eur J Pain. 2017; 21(2):264-77.
[52] Bowness JS, Burckett-St Laurent D, Hernandez N, Keane PA, Lobo C, Margetts S, et al. Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study. Br J Anaesth. 2023; 130(2):217-25.
[53] Bowness JS, Metcalfe D, El-Boghdadly K, Thurley N, Morecroft M, Hartley T, et al. Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines. Br J Anaesth. 2024.
[54] Ermer SC, Farney RJ, Johnson KB, Orr JA, Egan TD, Brewer LM. An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing. Anesth Analg. 2020; 130(5):1147-56.
[55] Rudolph JL, Marcantonio ER. Post-operative delirium: acute change with long-term implications. Anesth Analg. 2011; 112(5):1202-11.
[56] Liu Y, Shen W, Tian Z. Using machine learning algorithms to predict high-risk factors for post-operative delirium in elderly patients. Clin Interv Aging. 2023:157-68.
[57] Bishara A, Chiu C, Whitlock EL, Douglas VC, Lee S, Butte AJ, et al. Post-operative delirium prediction using machine learning models and preoperative electronic health record data. BMC anesthesiol. 2022; 22:1-12.
[58] Hackett NJ, De Oliveira GS, Jain UK, Kim JY. ASA class is a reliable independent predictor of medical complications and mortality following surgery. Int J Surg. 2015; 18:184-90.
[59] Chelazzi C, Villa G, Manno A, Ranfagni V, Gemmi E, Romagnoli S. The new SUMPOT to predict post-operative complications using an Artificial Neural Network. Sci Rep. 2021; 11(1):22692.
[60] Traeger L, Bedrikovetski S, Hanna JE, Moore JW, Sammour T. Machine learning prediction model for post-operative ileus following colorectal surgery. ANZ J Surg. 2024.
[61] Zhou CM, Li H, Xue Q, Yang JJ, Zhu Y. Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery. Heliyon. 2024.
[62] Liem VG, Hoeks SE, Mol KH, Potters JW, Grüne F, Stolker RJ, et al. Post-operative hypotension after noncardiac surgery and the association with myocardial injury. Anesthesiology. 2020; 133(3):510-22.
[63] Tol JTM, Terwindt LE, Rellum SR, Wijnberge M, van der Ster BJP, Kho E, et al. Performance of a Machine Learning Algorithm to Predict Hypotension in Spontaneously Breathing Non-Ventilated Post-Anesthesia and ICU Patients. J Pers Med. 2024; 14(2):210.
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How to Cite
1.
Sedigh Maroufi S, Sarkhosh M, Soleimani Movahed M, Behmanesh A, Ejmalian A. Revolutionizing Post Anesthesia Care Unit with Artificial Intelligence: A Narrative Review. Arch Anesth & Crit Care. 2024;.
