Artificial Intelligence Applications in Anesthesiology: A Comprehensive Narrative Review
Abstract
Background: The integration of artificial intelligence (AI) and machine learning (ML) technologies in anesthesiology has emerged as a transformative force in perioperative medicine. This narrative review synthesizes current evidence on AI applications across the perioperative continuum. The objective is to provide a comprehensive overview of AI applications in anesthesiology, examining current implementations, clinical outcomes, and future directions across preoperative assessment, intraoperative monitoring, drug delivery, postoperative care, and clinical documentation.
Methods: A systematic literature search was performed across several databases for peer-reviewed publications on AI applications in anesthesiology from 2015 to June, 2025. If the papers were peer reviewed and come from quality journals. Sixty-six papers from reputable peer-reviewed medical journals of high quality were selected for analysis.
Results: The use of AI-based applications for anesthesiology can be summarized into 5 specific domains: predictive modeling to predict surgical risk assessment, real-time monitoring systems that measure anesthesia depth, automated drug delivery systems (and devices), postoperative complication prediction after surgery, and natural language processing (NLP) to assess clinical documentation. Recent advances show better patient outcomes, clinical decision-making and operational efficiency.
Conclusion: AI technology holds substantial potential to enhance anesthesiology practice along the entire perioperative continuum. Although some challenges need to be addressed in respect of clinical integration and validation, we believe that there is sufficient evidence to justify undertaking further development work with a view towards implementation of AI-assisted anesthesia care.
[1] 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.
[2] Xu H, Fu C, Zhao W, Yan Z, Song S, et al. Anesthesia transformed: AI pioneering a new era in perioperative medicine. Anesthesiology and Perioperative Science. 2025; 3(1):6.
[3] Maheshwari K, Cywinski JB, Papay F, Khanna AK, Mathur P. Artificial Intelligence for Perioperative Medicine: Perioperative Intelligence. Anesth Analg. 2023; 136(4):637-45.
[4] Dabbagh A, Sabouri AS. The Role of Artificial Intelligence in Medicine with a Special Focus on Anesthesiology and Perioperative Care. Anesthesiol Clin. 2025; 43(3):389-403.
[5] Kambale M, Jadhav S. Applications of artificial intelligence in anesthesia: A systematic review. Saudi J Anaesth. 2024; 18(2):249-56.
[6] Ghasemi A, Naeimaeyi aali M. Clinical Reasoning and Artificial Intelligence. Ann Mil Health Sci Res. 2023; 21(1):e134440.
[7] Goldstein BA, Navar AM, Pencina MJ, Ioannidis JP. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J AM Med Inform Assoc. 2017; 24(1):198-208.
[8] Tsai ML, Chen KF, Chen PC. Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review. J Am Heart Assoc. 2025; 14(6):e036946.
[9] Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med. 2024; 30(5):1257-68.
[10] Shaikh TA, Rasool T, Verma P, Mir WA. A fundamental overview of ensemble deep learning models and applications: systematic literature and state of the art. Ann Oper Res. 2024; 1-77.
[11] Graeßner M, Jungwirth B, Frank E, Schaller SJ, Kochs E, et al. Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data. Sci Rep. 2023; 13(1):7128.
[12] Alafari F, Driss M, Cherif A. Advances in natural language processing for healthcare: A comprehensive review of techniques, applications, and future directions. Comput Sci Rev. 2025; 56:100725.
[13] Elhaddad M, Hamam S. AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential. Cureus. 2024; 16(4):e57728.
[14] Gracia Martínez JL, Pfang B, Morales Coca MÁ, Caramés Sánchez C, Del Olmo Rodríguez M, et al. Implementing a closed loop clinical decision support system for sustainable preoperative care. NPJ Digit Med. 2025; 8(1):6.
[15] Singam A. Revolutionizing Patient Care: A Comprehensive Review of Artificial Intelligence Applications in Anesthesia. Cureus. 2023; 15(12):e49887.
[16] Carneiro RA, Pereira LA. Depth of Anesthesia Monitoring and Artificial Intelligence. Curr Anesthesiol Rep. 2025; 15(1):19.
[17] Zhou C, Liu Y, An X, Xu X, Wang H. Optimization of deep learning architecture based on multi-path convolutional neural network algorithm. Sci Rep. 2025; 15(1):19532.
[18] Wang Y, Liu L, Wang C. Trends in using deep learning algorithms in biomedical prediction systems. Front Neurosci. 2023; 17:1256351.
[19] Elvas LB, Almeida A, Ferreira JC. The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review. JMIR Med Inform. 2025; 13:e64349.
[20] Greco M, Lubian M, Cecconi M. The future of artificial intelligence in cardiovascular monitoring. Curr Opin Crit Care. 2025; 31(3):354-9.
[21] Michard F, Mulder MP, Gonzalez F, Sanfilippo F. AI for the hemodynamic assessment of critically ill and surgical patients: focus on clinical applications. Ann Intensive Care. 2025; 15(1):26.
[22] Olawade DB, Aderinto N, Clement David-Olawade A, Egbon E, Adereni T, et al. Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges. Clin Neurol Neurosurg. 2025; 249:108689.
[23] Hanna MG, Pantanowitz L, Dash R, Harrison JH, Deebajah M, et al. Future of Artificial Intelligence-Machine Learning Trends in Pathology and Medicine. Mod Pathol. 2025; 38(4):100705.
[24] Krones F, Marikkar U, Parsons G, Szmul A, Mahdi A. Review of multimodal machine learning approaches in healthcare. Inf Fusion. 2025; 114:None.
[25] Kim S, Zeitzer JM, Mackey S, Darnall BD. Revealing sleep and pain reciprocity with wearables and machine learning. Commun Med (Lond). 2025; 5(1):160.
[26] Sadr H, Nazari M, Khodaverdian Z, Farzan R, Yousefzadeh-Chabok S, et al. Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches. Eur J Med Res. 2025; 30(1):418.
[27] Gordon L, Grantcharov T, Rudzicz F. Explainable Artificial Intelligence for Safe Intraoperative Decision Support. JAMA Surg. 2019; 154(11):1064-5.
[28] Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, et al. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput Biol Med. 2024; 178:108702.
[29] Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, et al. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020; 323(11):1052-1060.
[30] Satheeskumar R. Enhancing drug discovery with AI: Predictive modeling of pharmacokinetics using Graph Neural Networks and ensemble learning. Intelligent Pharmacy. 2025; 3(2):127-40.
[31] Abbott TEF, Pearse RM. Depth of Anesthesia and Postoperative Delirium. JAMA. 2019; 321(5):459-60.
[32] Janssen A, Leebeek FWG, Cnossen MH, Mathôt RAA; OPTI‐CLOT study group and SYMPHONY consortium. Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling. CPT Pharmacometrics Syst Pharmacol. 2022; 11(7):934-45.
[33] Navarrete-Welton AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020; 14(4):369-81.
[34] Feinstein M, Katz D, Demaria S, Hofer IS. Remote Monitoring and Artificial Intelligence: Outlook for 2050. Anesth Analg. 2024; 138(2):350-7.
[35] Binkley CE, Green BP. Does Intraoperative Artificial Intelligence Decision Support Pose Ethical Issues? JAMA Surg. 2021; 154(11):1064-5.
[36] Ren Y, Loftus TJ, Datta S, Ruppert MM, Guan Z, et al. Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform. JAMA Netw Open. 2022; 5(5):e2211973.
[37] Garg S, Kapoor MC. Role of artificial intelligence in perioperative monitoring in anaesthesia. Indian J Anaesth. 2024; 68(1):87-92.
[38] Chowdhury MR, Madanu R, Abbod MF, Fan SZ, Shieh JS. Deep learning via ECG and PPG signals for prediction of depth of anesthesia. Biomed Signal Process Control. 2021; 68:102663.
[39] Angus DC. Randomized Clinical Trials of Artificial Intelligence. JAMA. 2020; 323(11):1043-5.
[40] Zain Z, Almadhoun MKIK, Alsadoun L, Bokhari SFH. Leveraging Artificial Intelligence and Machine Learning to Optimize Enhanced Recovery After Surgery (ERAS) Protocols. Cureus. 2024; 16(3):e56668.
[41] Pahlevani M, Rajabi E, Taghavi M, VanBerkel P. Developing a decision support tool to predict delayed discharge from hospitals using machine learning. BMC Health Serv Res. 2025; 25(1):56.
[42] Lee C, Britto S, Diwan K. Evaluating the Impact of Artificial Intelligence (AI) on Clinical Documentation Efficiency and Accuracy Across Clinical Settings: A Scoping Review. Cureus. 2024; 16(11):e73994.
[43] Canales C, Lee C, Cannesson M. Science Without Conscience Is but the Ruin of the Soul: The Ethics of Big Data and Artificial Intelligence in Perioperative Medicine. Anesth Analg. 2020; 130(5):1234-43.
[44] Doyle DJ. Technology in anesthesia and perioperative medicine: exploring opportunities and challenges. Innovation in Anesthesiology. 2024; 1:7-18.
[45] Chen Z, Liang N, Zhang H, Li H, Yang Y, et al. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart. 2023; 10(2):e002432.
[46] Xia M. Artificial intelligence in airway management. In: Artificial Intelligence in Anesthesiology. Springer Nature Singapore; 2023. p. 41-54.
[47] Ren W, Chen J, Liu J, Fu Z, Yao Y, et al. Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network. Heliyon. 2022; 9(1):e12481.
[48] Davoud SC, Kovacheva VP. On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology. Curr Anesthesiol Rep. 2023; 13(2):31-40.
[49] Khazaei H, McGregor C, Eklund JM, El-Khatib K. Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework. JMIR Med Inform. 2015; 3(4):e36.
[50] Khayatan N, Aslani N, Garavand A, Galehdar N, Khayatan V. Information Requirements of Clinical Decision Support Systems for the Diagnosis and Prediction of Preeclampsia. Shiraz E Med J. 2025; 26(2):1.
[51] Hemmerling TM, Jeffries SD. Robotic Anesthesia: A Vision for 2050. Anesth Analg. 2024; 138(2):239-51.
[52] Lonsdale H, Jalali A, Ahumada L, Matava C. Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care. J Pediatr. 2020; 221S:S3-S10.
[53] Carabantes M. Black-box artificial intelligence: an epistemological and critical analysis. AI Soc. 2020; 35(2):309-17.
[54] Jamali N, Razavi H, Gharib MR. Optimization of propofol dose estimated during anesthesia through artificial intelligence by genetic algorithm: design and clinical assessment. Neural Process Lett. 2022; 54(4):3019-43.
[55] Banerjee S, Abhishek HN, Gupta P, Patel AP, Kant K, et al. Artificial Intelligence in anesthesia: Biotechnology applications for optimal patient outcomes. J Cell Biotechnol. 2023; 9(2):85-91.
[56] Yurdem B, Kuzlu M, Gullu MK, Catak FO, Tabassum M. Federated learning: Overview, strategies, applications, tools and future directions. Heliyon. 2024; 10(19):e38137.
[57] IBM. What is reinforcement learning? [Internet]. 2025 [cited 2025 Oct 2]. Available from: https://www.ibm.com/think/topics/reinforcement-learning
[58] IBM. What is Explainable AI (XAI)? [Internet]. 2025 [cited 2025 Oct 2]. Available from: https://www.ibm.com/think/topics/explainable-ai
[59] Orbatu D, Peker Bulğan Zİ, Olmez E, Er O. A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients. Iran J Pediatr. 2025; 35(2):e148520.
[60] Singhal M, Gupta L, Hirani K. A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia. Cureus. 2023; 15(9):e45038.
[61] Park I, Park JH, Yoon J, Na HS, Oh AY, et al. Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study. Korean J Anesthesiol. 2024; 77(2):195-204.
[62] Liao Y, Thompson C, Peterson S, Mandrola J, Beg MS. The Future of Wearable Technologies and Remote Monitoring in Health Care. Am Soc Clin Oncol Educ Book. 2019; 39:115-21.
[63] Mahajan A, Heydari K, Powell D. Wearable AI to enhance patient safety and clinical decision-making. NPJ Digit Med. 2025; 8(1):176.
[64] Mathis MR, Kheterpal S, Najarian K. Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark. Anesthesiology. 2018; 129(4):619-22.
[65] Zeng S, Qing Q, Xu W, Yu S, Zheng M, et al. Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors. Front Med (Lausanne). 2024; 11:1365524.
[66] Molla G, Bitew M. Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives. Biomedicines. 2024; 12(12):2750.
[2] Xu H, Fu C, Zhao W, Yan Z, Song S, et al. Anesthesia transformed: AI pioneering a new era in perioperative medicine. Anesthesiology and Perioperative Science. 2025; 3(1):6.
[3] Maheshwari K, Cywinski JB, Papay F, Khanna AK, Mathur P. Artificial Intelligence for Perioperative Medicine: Perioperative Intelligence. Anesth Analg. 2023; 136(4):637-45.
[4] Dabbagh A, Sabouri AS. The Role of Artificial Intelligence in Medicine with a Special Focus on Anesthesiology and Perioperative Care. Anesthesiol Clin. 2025; 43(3):389-403.
[5] Kambale M, Jadhav S. Applications of artificial intelligence in anesthesia: A systematic review. Saudi J Anaesth. 2024; 18(2):249-56.
[6] Ghasemi A, Naeimaeyi aali M. Clinical Reasoning and Artificial Intelligence. Ann Mil Health Sci Res. 2023; 21(1):e134440.
[7] Goldstein BA, Navar AM, Pencina MJ, Ioannidis JP. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J AM Med Inform Assoc. 2017; 24(1):198-208.
[8] Tsai ML, Chen KF, Chen PC. Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review. J Am Heart Assoc. 2025; 14(6):e036946.
[9] Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med. 2024; 30(5):1257-68.
[10] Shaikh TA, Rasool T, Verma P, Mir WA. A fundamental overview of ensemble deep learning models and applications: systematic literature and state of the art. Ann Oper Res. 2024; 1-77.
[11] Graeßner M, Jungwirth B, Frank E, Schaller SJ, Kochs E, et al. Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data. Sci Rep. 2023; 13(1):7128.
[12] Alafari F, Driss M, Cherif A. Advances in natural language processing for healthcare: A comprehensive review of techniques, applications, and future directions. Comput Sci Rev. 2025; 56:100725.
[13] Elhaddad M, Hamam S. AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential. Cureus. 2024; 16(4):e57728.
[14] Gracia Martínez JL, Pfang B, Morales Coca MÁ, Caramés Sánchez C, Del Olmo Rodríguez M, et al. Implementing a closed loop clinical decision support system for sustainable preoperative care. NPJ Digit Med. 2025; 8(1):6.
[15] Singam A. Revolutionizing Patient Care: A Comprehensive Review of Artificial Intelligence Applications in Anesthesia. Cureus. 2023; 15(12):e49887.
[16] Carneiro RA, Pereira LA. Depth of Anesthesia Monitoring and Artificial Intelligence. Curr Anesthesiol Rep. 2025; 15(1):19.
[17] Zhou C, Liu Y, An X, Xu X, Wang H. Optimization of deep learning architecture based on multi-path convolutional neural network algorithm. Sci Rep. 2025; 15(1):19532.
[18] Wang Y, Liu L, Wang C. Trends in using deep learning algorithms in biomedical prediction systems. Front Neurosci. 2023; 17:1256351.
[19] Elvas LB, Almeida A, Ferreira JC. The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review. JMIR Med Inform. 2025; 13:e64349.
[20] Greco M, Lubian M, Cecconi M. The future of artificial intelligence in cardiovascular monitoring. Curr Opin Crit Care. 2025; 31(3):354-9.
[21] Michard F, Mulder MP, Gonzalez F, Sanfilippo F. AI for the hemodynamic assessment of critically ill and surgical patients: focus on clinical applications. Ann Intensive Care. 2025; 15(1):26.
[22] Olawade DB, Aderinto N, Clement David-Olawade A, Egbon E, Adereni T, et al. Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges. Clin Neurol Neurosurg. 2025; 249:108689.
[23] Hanna MG, Pantanowitz L, Dash R, Harrison JH, Deebajah M, et al. Future of Artificial Intelligence-Machine Learning Trends in Pathology and Medicine. Mod Pathol. 2025; 38(4):100705.
[24] Krones F, Marikkar U, Parsons G, Szmul A, Mahdi A. Review of multimodal machine learning approaches in healthcare. Inf Fusion. 2025; 114:None.
[25] Kim S, Zeitzer JM, Mackey S, Darnall BD. Revealing sleep and pain reciprocity with wearables and machine learning. Commun Med (Lond). 2025; 5(1):160.
[26] Sadr H, Nazari M, Khodaverdian Z, Farzan R, Yousefzadeh-Chabok S, et al. Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches. Eur J Med Res. 2025; 30(1):418.
[27] Gordon L, Grantcharov T, Rudzicz F. Explainable Artificial Intelligence for Safe Intraoperative Decision Support. JAMA Surg. 2019; 154(11):1064-5.
[28] Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, et al. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput Biol Med. 2024; 178:108702.
[29] Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, et al. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020; 323(11):1052-1060.
[30] Satheeskumar R. Enhancing drug discovery with AI: Predictive modeling of pharmacokinetics using Graph Neural Networks and ensemble learning. Intelligent Pharmacy. 2025; 3(2):127-40.
[31] Abbott TEF, Pearse RM. Depth of Anesthesia and Postoperative Delirium. JAMA. 2019; 321(5):459-60.
[32] Janssen A, Leebeek FWG, Cnossen MH, Mathôt RAA; OPTI‐CLOT study group and SYMPHONY consortium. Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling. CPT Pharmacometrics Syst Pharmacol. 2022; 11(7):934-45.
[33] Navarrete-Welton AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. 2020; 14(4):369-81.
[34] Feinstein M, Katz D, Demaria S, Hofer IS. Remote Monitoring and Artificial Intelligence: Outlook for 2050. Anesth Analg. 2024; 138(2):350-7.
[35] Binkley CE, Green BP. Does Intraoperative Artificial Intelligence Decision Support Pose Ethical Issues? JAMA Surg. 2021; 154(11):1064-5.
[36] Ren Y, Loftus TJ, Datta S, Ruppert MM, Guan Z, et al. Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform. JAMA Netw Open. 2022; 5(5):e2211973.
[37] Garg S, Kapoor MC. Role of artificial intelligence in perioperative monitoring in anaesthesia. Indian J Anaesth. 2024; 68(1):87-92.
[38] Chowdhury MR, Madanu R, Abbod MF, Fan SZ, Shieh JS. Deep learning via ECG and PPG signals for prediction of depth of anesthesia. Biomed Signal Process Control. 2021; 68:102663.
[39] Angus DC. Randomized Clinical Trials of Artificial Intelligence. JAMA. 2020; 323(11):1043-5.
[40] Zain Z, Almadhoun MKIK, Alsadoun L, Bokhari SFH. Leveraging Artificial Intelligence and Machine Learning to Optimize Enhanced Recovery After Surgery (ERAS) Protocols. Cureus. 2024; 16(3):e56668.
[41] Pahlevani M, Rajabi E, Taghavi M, VanBerkel P. Developing a decision support tool to predict delayed discharge from hospitals using machine learning. BMC Health Serv Res. 2025; 25(1):56.
[42] Lee C, Britto S, Diwan K. Evaluating the Impact of Artificial Intelligence (AI) on Clinical Documentation Efficiency and Accuracy Across Clinical Settings: A Scoping Review. Cureus. 2024; 16(11):e73994.
[43] Canales C, Lee C, Cannesson M. Science Without Conscience Is but the Ruin of the Soul: The Ethics of Big Data and Artificial Intelligence in Perioperative Medicine. Anesth Analg. 2020; 130(5):1234-43.
[44] Doyle DJ. Technology in anesthesia and perioperative medicine: exploring opportunities and challenges. Innovation in Anesthesiology. 2024; 1:7-18.
[45] Chen Z, Liang N, Zhang H, Li H, Yang Y, et al. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart. 2023; 10(2):e002432.
[46] Xia M. Artificial intelligence in airway management. In: Artificial Intelligence in Anesthesiology. Springer Nature Singapore; 2023. p. 41-54.
[47] Ren W, Chen J, Liu J, Fu Z, Yao Y, et al. Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network. Heliyon. 2022; 9(1):e12481.
[48] Davoud SC, Kovacheva VP. On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology. Curr Anesthesiol Rep. 2023; 13(2):31-40.
[49] Khazaei H, McGregor C, Eklund JM, El-Khatib K. Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework. JMIR Med Inform. 2015; 3(4):e36.
[50] Khayatan N, Aslani N, Garavand A, Galehdar N, Khayatan V. Information Requirements of Clinical Decision Support Systems for the Diagnosis and Prediction of Preeclampsia. Shiraz E Med J. 2025; 26(2):1.
[51] Hemmerling TM, Jeffries SD. Robotic Anesthesia: A Vision for 2050. Anesth Analg. 2024; 138(2):239-51.
[52] Lonsdale H, Jalali A, Ahumada L, Matava C. Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care. J Pediatr. 2020; 221S:S3-S10.
[53] Carabantes M. Black-box artificial intelligence: an epistemological and critical analysis. AI Soc. 2020; 35(2):309-17.
[54] Jamali N, Razavi H, Gharib MR. Optimization of propofol dose estimated during anesthesia through artificial intelligence by genetic algorithm: design and clinical assessment. Neural Process Lett. 2022; 54(4):3019-43.
[55] Banerjee S, Abhishek HN, Gupta P, Patel AP, Kant K, et al. Artificial Intelligence in anesthesia: Biotechnology applications for optimal patient outcomes. J Cell Biotechnol. 2023; 9(2):85-91.
[56] Yurdem B, Kuzlu M, Gullu MK, Catak FO, Tabassum M. Federated learning: Overview, strategies, applications, tools and future directions. Heliyon. 2024; 10(19):e38137.
[57] IBM. What is reinforcement learning? [Internet]. 2025 [cited 2025 Oct 2]. Available from: https://www.ibm.com/think/topics/reinforcement-learning
[58] IBM. What is Explainable AI (XAI)? [Internet]. 2025 [cited 2025 Oct 2]. Available from: https://www.ibm.com/think/topics/explainable-ai
[59] Orbatu D, Peker Bulğan Zİ, Olmez E, Er O. A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients. Iran J Pediatr. 2025; 35(2):e148520.
[60] Singhal M, Gupta L, Hirani K. A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia. Cureus. 2023; 15(9):e45038.
[61] Park I, Park JH, Yoon J, Na HS, Oh AY, et al. Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study. Korean J Anesthesiol. 2024; 77(2):195-204.
[62] Liao Y, Thompson C, Peterson S, Mandrola J, Beg MS. The Future of Wearable Technologies and Remote Monitoring in Health Care. Am Soc Clin Oncol Educ Book. 2019; 39:115-21.
[63] Mahajan A, Heydari K, Powell D. Wearable AI to enhance patient safety and clinical decision-making. NPJ Digit Med. 2025; 8(1):176.
[64] Mathis MR, Kheterpal S, Najarian K. Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know: More than Black Magic for the Art of the Dark. Anesthesiology. 2018; 129(4):619-22.
[65] Zeng S, Qing Q, Xu W, Yu S, Zheng M, et al. Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors. Front Med (Lausanne). 2024; 11:1365524.
[66] Molla G, Bitew M. Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives. Biomedicines. 2024; 12(12):2750.
| Files | ||
| Issue | Article in Press |
|
| Section | Review Article(s) | |
| Keywords | ||
| artificial intelligence machine learning perioperative medicine clinical decision support systems. | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Abtahi D. Artificial Intelligence Applications in Anesthesiology: A Comprehensive Narrative Review. Arch Anesth & Crit Care. 2026;.
