*Result*: The Potential Impacts of Artificial Intelligence on Preoperative Optimization and Predicting Risks of Morbidity and Mortality: A Narrative Focused Review.
Title:
The Potential Impacts of Artificial Intelligence on Preoperative Optimization and Predicting Risks of Morbidity and Mortality: A Narrative Focused Review.
Authors:
Source:
A&A practice [A A Pract] 2025 Oct 10; Vol. 19 (10), pp. e02061. Date of Electronic Publication: 2025 Oct 10 (Print Publication: 2025).
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Wolters Kluwer Health, Inc Country of Publication: United States NLM ID: 101714112 Publication Model: eCollection Cited Medium: Internet ISSN: 2575-3126 (Electronic) Linking ISSN: 25753126 NLM ISO Abbreviation: A A Pract Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Philadelphia, PA] : Wolters Kluwer Health, Inc., [2018]-
MeSH Terms:
References:
Ferschl MB, Tung A, Sweitzer B, Huo D, Glick DB. Preoperative clinic visits reduce operating room cancellations and delays. Anesthesiology. 2005;103:855–859.
Correll DJ, Bader AM, Hull MW, Hsu C, Tsen LC, Hepner DL. Value of preoperative clinic visits in identifying issues with potential impact on operating room efficiency. Anesthesiology. 2006;105:1254–1259.
Blitz JD, Kendale SM, Jain SK, Cuff GE, Kim JT, Rosenberg AD. Preoperative evaluation clinic visit is associated with decreased risk of in-hospital postoperative mortality. Anesthesiology. 2016;125:280–294.
Nelson SE, Li G, Shi H, Terekhov M, Ehrenfeld JM, Wanderer JP. The impact of reduction of testing at a preoperative evaluation clinic for elective cases: value added without adverse outcomes. J Clin Anesth. 2019;55:92–99.
Lee JY, Kim HT, Choi JH, et al. Effect of clinical anesthesia preoperative evaluation on the length of preoperative period and total hospitalization of patients undergoing laparoscopic cholecystectomy. Am J Transl Res. 2021;13:11943–11947.
Hofer IS, Cheng D, Grogan T. A retrospective analysis demonstrates that a failure to document key comorbid diseases in the anesthesia preoperative evaluation associates with increased length of stay and mortality. Anesth Analg. 2021;133:698–706.
Xue B, Li D, Lu C, et al. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw Open. 2021;4:e212240.
Chiew CJ, Liu N, Wong TH, Sim YE, Abdullah HR. Utilizing machine learning methods for preoperative prediction of postsurgical mortality and intensive care unit admission. Ann Surg. 2020;272:1133–1139.
Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T, Budoff M, Naghavi M. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc. 2018;7:e009476.
Zeng Q, Shao Y, Cheng Y, et al. Extracting frailty status for post surgical mortality prediction. 2018; International Conferences e-Health 2018; ICT, Society, and Human Beings 2018; and Web Based Communities and Social Media 2018.
Olano-Espinosa E, Avila-Tomas JF, Minue-Lorenzo C, et al.; Dejal@ Group. Effectiveness of a Conversational Chatbot (Dejal@bot) for the adult population to quit smoking: pragmatic, multicenter, controlled, randomized clinical trial in primary care. JMIR Mhealth Uhealth. 2022;10:e34273.
Masaki K, Tateno H, Nomura A, et al. A randomized controlled trial of a smoking cessation smartphone application with a carbon monoxide checker. NPJ Digit Med. 2020;3:35.
Ghosh A, Freda PJ, Shahrestani S, et al. Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion. Spine J. 2025;25:1596–1607.
Zang H, Hu A, Xu X, Ren H, Xu L. Development of machine learning models to predict perioperative blood transfusion in hip surgery. BMC Med Inform Decis Mak. 2024;24:158.
Seong H, Lee KS, Choi Y, et al. Explainable artificial intelligence for predicting red blood cell transfusion in geriatric patients undergoing hip arthroplasty: machine learning analysis using national health insurance data. Medicine (Baltim). 2024;103:e36909.
Shi X, Cui Y, Wang S, Pan Y, Wang B, Lei M. Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques. Spine J. 2024;24:146–160.
Jørgensen CC, Michelsen C, Petersen T, Kehlet H. Preoperative hemoglobin thresholds for increased risk of “medical” complications in fast-track total hip and knee arthroplasty, a secondary analysis of a machine-learning algorithm. Transfusion. 2024;64:438–442.
Gonzalez-Estrada A, Park MA, Accarino JJO, et al. Predicting penicillin allergy: A United States Multicenter Retrospective Study. J Allergy Clin Immunol Pract. 2024;12:1181–1191.e10.
Stretton B, Jiang M, Kovoor J, et al. Artificial intelligence-enabled penicillin allergy delabelling: an implementation study. Intern Med J. 2023;53:2119–2122.
Jiang M, Lam A, Lam L, et al. Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients. Br J Neurosurg. 2025;39:40–43.
Zhang Y, Fatemi P, Medress Z, et al. A predictive-modeling based screening tool for prolonged opioid use after surgical management of low back and lower extremity pain. Spine J. 2020;20:1184–1195.
Klemt C, Harvey MJ, Robinson MG, Esposito JG, Yeo I, Kwon YM. Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. 2022;30:2573–2581.
Gabriel RA, Harjai B, Prasad RS, et al. Machine learning approach to predicting persistent opioid use following lower extremity joint arthroplasty. Reg Anesth Pain Med. 2022;47:313–319.
Lu Y, Forlenza E, Wilbur RR, et al. Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy. Knee Surg Sports Traumatol Arthrosc. 2022;30:762–772.
Gopalakrishnan C, Desai RJ, Franklin JM, et al. Development of a Medicare Claims-based model to predict persistent high-dose opioid use after total knee replacement. Arthritis Care Res (Hoboken). 2022;74:1342–1348.
Karhade AV, Schwab JH, Bedair HS. Development of machine learning algorithms for prediction of sustained postoperative opioid prescriptions after total hip arthroplasty. J Arthroplasty. 2019;34:2272–2277.e1.
Kunze KN, Polce EM, Alter TD, Nho SJ. Machine learning algorithms predict prolonged opioid use in opioid-naïve primary hip arthroscopy patients. J Am Acad Orthop Surg Glob Res Rev. 2021;5:e21.00093–e21.00098.
Gabriel RA, Simpson S, Zhong W, Burton BN, Mehdipour S, Said ET. A neural network model using pain score patterns to predict the need for outpatient opioid refills following ambulatory surgery: algorithm development and validation. JMIR Perioper Med. 2023;6:e40455.
Bai C, Al-Ani M, Amini S, et al. Developing and validating an electronic health record-based frailty index in pre-operative settings using machine learning. J Intellig Inform Sys. 2024;62:339–354.
Lee SW, Lee EH, Choi IC. An ensemble machine learning approach to predict postoperative mortality in older patients undergoing emergency surgery. BMC Geriatr. 2023;23:262.
Mardini M, Price C, Tighe P, Manini T. Explainable machine-learning for predicting preoperative frailty phenotype using electronic health records. Innov Aging. 2022;6(Supplement_1):564–564.
Chen Y, Zhou J, Kai Chan JS, et al. Development of an electronic frailty index for predicting mortality in patients undergoing transcatheter aortic valve replacement using machine learning. Ann Clin Cardiol. 2023;5:17–26.
Kramer D, Jauk S, Veeranki S, et al. Machine learning-based prediction of malnutrition in surgical in-patients: a validation pilot study. Stud Health Technol Inform. 2024;313:156–157.
Sharma V, Fadel A, Tollefson MK, et al. Artificial intelligence-based assessment of preoperative body composition is associated with early complications after radical cystectomy. J Urol. 2025;213:228–237.
Ding Z, Zhang L, Zhang Y, et al. A supervised explainable machine learning model for perioperative neurocognitive disorder in liver-transplantation patients and external validation on the medical information mart for intensive care IV database: retrospective study. J Med Internet Res. 2025;27:e55046.
Dai R, Yang K, Zhuang J, et al. Enhanced machine learning approaches for OSA patient screening: model development and validation study. Sci Rep. 2024;14:19756.
Lee JC, Hamill CS, Shnayder Y, Buczek E, Kakarala K, Bur AM. Exploring the role of artificial intelligence chatbots in preoperative counseling for head and neck cancer surgery. Laryngoscope. 2024;134:2757–2761.
Vydiswaran VGV, Strayhorn A, Weber K, et al. Automated-detection of risky alcohol use prior to surgery using natural language processing. Alcohol Clin Exp Res (Hoboken). 2024;48:153–163.
Anderson AE, Kerr WT, Thames A, Li T, Xiao J, Cohen MS. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: a cross-sectional, unselected, retrospective study. J Biomed Inform. 2016;60:162–168.
Musallam KM, Tamim HM, Richards T, et al. Preoperative anaemia and postoperative outcomes in non-cardiac surgery: a retrospective cohort study. Lancet. 2011;378:1396–1407.
Beattie WS, Karkouti K, Wijeysundera DN, Tait G. Risk associated with preoperative anemia in noncardiac surgery: a single-center cohort study. Anesthesiology. 2009;110:574–581.
Buchalter DB, Nduaguba A, Teo GM, Kugelman D, Aggarwal VK, Long WJ. Cefazolin remains the linchpin for preventing acute periprosthetic joint infection following primary total knee arthroplasty. Bone Jt Open. 2022;3:35–41.
Simpson S, Zhong W, Mehdipour S, . Classifying High-risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach. Anesth Analg. 2024;139:690–699.
Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15:29.
Guan Z, Li H, Liu R, et al. Artificial intelligence in diabetes management: advancements, opportunities, and challenges. Cell Rep Med. 2023;4:101213.
Abhari S, Niakan Kalhori SR, Ebrahimi M, Hasannejadasl H, Garavand A. Artificial intelligence applications in type 2 diabetes mellitus care: focus on machine learning methods. Healthc Inform Res. 2019;25:248–261.
Teo WW, Ti LK, Lean LL, et al. The neglected perioperative population of undiagnosed diabetics—a retrospective cohort study. BMC Surg. 2020;20:188.
Fried LP, Tangen CM, Walston J, et al.; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–M156.
Visco V, Izzo C, Mancusi C, et al. Artificial intelligence in hypertension management: an ace up your sleeve. J Cardiovasc Dev Dis. 2023;10:74.
Golden A. Theoretical framework for an artificial intelligence-based comprehensive geriatric assessment. Innov Aging. 2023;7(Supplement_1):877–877.
Brennan HL, Kirby SD. The role of artificial intelligence in the treatment of obstructive sleep apnea. J Otolaryngol Head Neck Surg. 2023;52:7.
Bendotti H, Lawler S, Chan GCK, Gartner C, Ireland D, Marshall HM. Conversational artificial intelligence interventions to support smoking cessation: a systematic review and meta-analysis. Digit Health. 2023;9:20552076231211634.
Wang H, Zhang Q, Ip TK, Lau J. Social Media–based conversational agents for health management and interventions. Computer. 2018;51:26–33.
Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021;22:122.
Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: a call for open science. Patterns (N Y). 2021;2:100347.
Chen M, Zhang B, Cai Z, et al. Acceptance of clinical artificial intelligence among physicians and medical students: a systematic review with cross-sectional survey. Front Med (Lausanne). 2022;9:990604.
Correll DJ, Bader AM, Hull MW, Hsu C, Tsen LC, Hepner DL. Value of preoperative clinic visits in identifying issues with potential impact on operating room efficiency. Anesthesiology. 2006;105:1254–1259.
Blitz JD, Kendale SM, Jain SK, Cuff GE, Kim JT, Rosenberg AD. Preoperative evaluation clinic visit is associated with decreased risk of in-hospital postoperative mortality. Anesthesiology. 2016;125:280–294.
Nelson SE, Li G, Shi H, Terekhov M, Ehrenfeld JM, Wanderer JP. The impact of reduction of testing at a preoperative evaluation clinic for elective cases: value added without adverse outcomes. J Clin Anesth. 2019;55:92–99.
Lee JY, Kim HT, Choi JH, et al. Effect of clinical anesthesia preoperative evaluation on the length of preoperative period and total hospitalization of patients undergoing laparoscopic cholecystectomy. Am J Transl Res. 2021;13:11943–11947.
Hofer IS, Cheng D, Grogan T. A retrospective analysis demonstrates that a failure to document key comorbid diseases in the anesthesia preoperative evaluation associates with increased length of stay and mortality. Anesth Analg. 2021;133:698–706.
Xue B, Li D, Lu C, et al. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw Open. 2021;4:e212240.
Chiew CJ, Liu N, Wong TH, Sim YE, Abdullah HR. Utilizing machine learning methods for preoperative prediction of postsurgical mortality and intensive care unit admission. Ann Surg. 2020;272:1133–1139.
Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T, Budoff M, Naghavi M. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc. 2018;7:e009476.
Zeng Q, Shao Y, Cheng Y, et al. Extracting frailty status for post surgical mortality prediction. 2018; International Conferences e-Health 2018; ICT, Society, and Human Beings 2018; and Web Based Communities and Social Media 2018.
Olano-Espinosa E, Avila-Tomas JF, Minue-Lorenzo C, et al.; Dejal@ Group. Effectiveness of a Conversational Chatbot (Dejal@bot) for the adult population to quit smoking: pragmatic, multicenter, controlled, randomized clinical trial in primary care. JMIR Mhealth Uhealth. 2022;10:e34273.
Masaki K, Tateno H, Nomura A, et al. A randomized controlled trial of a smoking cessation smartphone application with a carbon monoxide checker. NPJ Digit Med. 2020;3:35.
Ghosh A, Freda PJ, Shahrestani S, et al. Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion. Spine J. 2025;25:1596–1607.
Zang H, Hu A, Xu X, Ren H, Xu L. Development of machine learning models to predict perioperative blood transfusion in hip surgery. BMC Med Inform Decis Mak. 2024;24:158.
Seong H, Lee KS, Choi Y, et al. Explainable artificial intelligence for predicting red blood cell transfusion in geriatric patients undergoing hip arthroplasty: machine learning analysis using national health insurance data. Medicine (Baltim). 2024;103:e36909.
Shi X, Cui Y, Wang S, Pan Y, Wang B, Lei M. Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques. Spine J. 2024;24:146–160.
Jørgensen CC, Michelsen C, Petersen T, Kehlet H. Preoperative hemoglobin thresholds for increased risk of “medical” complications in fast-track total hip and knee arthroplasty, a secondary analysis of a machine-learning algorithm. Transfusion. 2024;64:438–442.
Gonzalez-Estrada A, Park MA, Accarino JJO, et al. Predicting penicillin allergy: A United States Multicenter Retrospective Study. J Allergy Clin Immunol Pract. 2024;12:1181–1191.e10.
Stretton B, Jiang M, Kovoor J, et al. Artificial intelligence-enabled penicillin allergy delabelling: an implementation study. Intern Med J. 2023;53:2119–2122.
Jiang M, Lam A, Lam L, et al. Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients. Br J Neurosurg. 2025;39:40–43.
Zhang Y, Fatemi P, Medress Z, et al. A predictive-modeling based screening tool for prolonged opioid use after surgical management of low back and lower extremity pain. Spine J. 2020;20:1184–1195.
Klemt C, Harvey MJ, Robinson MG, Esposito JG, Yeo I, Kwon YM. Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. 2022;30:2573–2581.
Gabriel RA, Harjai B, Prasad RS, et al. Machine learning approach to predicting persistent opioid use following lower extremity joint arthroplasty. Reg Anesth Pain Med. 2022;47:313–319.
Lu Y, Forlenza E, Wilbur RR, et al. Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy. Knee Surg Sports Traumatol Arthrosc. 2022;30:762–772.
Gopalakrishnan C, Desai RJ, Franklin JM, et al. Development of a Medicare Claims-based model to predict persistent high-dose opioid use after total knee replacement. Arthritis Care Res (Hoboken). 2022;74:1342–1348.
Karhade AV, Schwab JH, Bedair HS. Development of machine learning algorithms for prediction of sustained postoperative opioid prescriptions after total hip arthroplasty. J Arthroplasty. 2019;34:2272–2277.e1.
Kunze KN, Polce EM, Alter TD, Nho SJ. Machine learning algorithms predict prolonged opioid use in opioid-naïve primary hip arthroscopy patients. J Am Acad Orthop Surg Glob Res Rev. 2021;5:e21.00093–e21.00098.
Gabriel RA, Simpson S, Zhong W, Burton BN, Mehdipour S, Said ET. A neural network model using pain score patterns to predict the need for outpatient opioid refills following ambulatory surgery: algorithm development and validation. JMIR Perioper Med. 2023;6:e40455.
Bai C, Al-Ani M, Amini S, et al. Developing and validating an electronic health record-based frailty index in pre-operative settings using machine learning. J Intellig Inform Sys. 2024;62:339–354.
Lee SW, Lee EH, Choi IC. An ensemble machine learning approach to predict postoperative mortality in older patients undergoing emergency surgery. BMC Geriatr. 2023;23:262.
Mardini M, Price C, Tighe P, Manini T. Explainable machine-learning for predicting preoperative frailty phenotype using electronic health records. Innov Aging. 2022;6(Supplement_1):564–564.
Chen Y, Zhou J, Kai Chan JS, et al. Development of an electronic frailty index for predicting mortality in patients undergoing transcatheter aortic valve replacement using machine learning. Ann Clin Cardiol. 2023;5:17–26.
Kramer D, Jauk S, Veeranki S, et al. Machine learning-based prediction of malnutrition in surgical in-patients: a validation pilot study. Stud Health Technol Inform. 2024;313:156–157.
Sharma V, Fadel A, Tollefson MK, et al. Artificial intelligence-based assessment of preoperative body composition is associated with early complications after radical cystectomy. J Urol. 2025;213:228–237.
Ding Z, Zhang L, Zhang Y, et al. A supervised explainable machine learning model for perioperative neurocognitive disorder in liver-transplantation patients and external validation on the medical information mart for intensive care IV database: retrospective study. J Med Internet Res. 2025;27:e55046.
Dai R, Yang K, Zhuang J, et al. Enhanced machine learning approaches for OSA patient screening: model development and validation study. Sci Rep. 2024;14:19756.
Lee JC, Hamill CS, Shnayder Y, Buczek E, Kakarala K, Bur AM. Exploring the role of artificial intelligence chatbots in preoperative counseling for head and neck cancer surgery. Laryngoscope. 2024;134:2757–2761.
Vydiswaran VGV, Strayhorn A, Weber K, et al. Automated-detection of risky alcohol use prior to surgery using natural language processing. Alcohol Clin Exp Res (Hoboken). 2024;48:153–163.
Anderson AE, Kerr WT, Thames A, Li T, Xiao J, Cohen MS. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: a cross-sectional, unselected, retrospective study. J Biomed Inform. 2016;60:162–168.
Musallam KM, Tamim HM, Richards T, et al. Preoperative anaemia and postoperative outcomes in non-cardiac surgery: a retrospective cohort study. Lancet. 2011;378:1396–1407.
Beattie WS, Karkouti K, Wijeysundera DN, Tait G. Risk associated with preoperative anemia in noncardiac surgery: a single-center cohort study. Anesthesiology. 2009;110:574–581.
Buchalter DB, Nduaguba A, Teo GM, Kugelman D, Aggarwal VK, Long WJ. Cefazolin remains the linchpin for preventing acute periprosthetic joint infection following primary total knee arthroplasty. Bone Jt Open. 2022;3:35–41.
Simpson S, Zhong W, Mehdipour S, . Classifying High-risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach. Anesth Analg. 2024;139:690–699.
Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15:29.
Guan Z, Li H, Liu R, et al. Artificial intelligence in diabetes management: advancements, opportunities, and challenges. Cell Rep Med. 2023;4:101213.
Abhari S, Niakan Kalhori SR, Ebrahimi M, Hasannejadasl H, Garavand A. Artificial intelligence applications in type 2 diabetes mellitus care: focus on machine learning methods. Healthc Inform Res. 2019;25:248–261.
Teo WW, Ti LK, Lean LL, et al. The neglected perioperative population of undiagnosed diabetics—a retrospective cohort study. BMC Surg. 2020;20:188.
Fried LP, Tangen CM, Walston J, et al.; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–M156.
Visco V, Izzo C, Mancusi C, et al. Artificial intelligence in hypertension management: an ace up your sleeve. J Cardiovasc Dev Dis. 2023;10:74.
Golden A. Theoretical framework for an artificial intelligence-based comprehensive geriatric assessment. Innov Aging. 2023;7(Supplement_1):877–877.
Brennan HL, Kirby SD. The role of artificial intelligence in the treatment of obstructive sleep apnea. J Otolaryngol Head Neck Surg. 2023;52:7.
Bendotti H, Lawler S, Chan GCK, Gartner C, Ireland D, Marshall HM. Conversational artificial intelligence interventions to support smoking cessation: a systematic review and meta-analysis. Digit Health. 2023;9:20552076231211634.
Wang H, Zhang Q, Ip TK, Lau J. Social Media–based conversational agents for health management and interventions. Computer. 2018;51:26–33.
Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021;22:122.
Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: a call for open science. Patterns (N Y). 2021;2:100347.
Chen M, Zhang B, Cai Z, et al. Acceptance of clinical artificial intelligence among physicians and medical students: a systematic review with cross-sectional survey. Front Med (Lausanne). 2022;9:990604.
Entry Date(s):
Date Created: 20251013 Date Completed: 20251013 Latest Revision: 20251013
Update Code:
20260130
DOI:
10.1213/XAA.0000000000002061
PMID:
41082382
Database:
MEDLINE
*Further Information*
*Preoperative optimization clinics enhance surgical outcomes by optimizing patients' health, reducing unnecessary tests, and minimizing cancellations. Artificial intelligence (AI) now promises to further advance these efforts by improving predictive modeling, automating risk assessments, and enabling personalized care strategies. AI models have shown particular strength in predicting postoperative complications, such as cardiovascular events. Tools like natural language processing (NLP) have also improved risk detection for conditions like alcohol misuse. The historical development of preoperative clinics-from Dr. J Alfred Lee's initial concept in 1949 to modern models like Enhanced Recovery After Surgery (ERAS) and Anesthesia Preoperative Evaluation Clinic (APEC)-laid the groundwork for today's integration of electronic health records (EHRs) and decision-support systems, now evolving toward AI-driven care. Machine-learning algorithms have proven superior to traditional models for predicting postoperative anemia, opioid dependence, diabetes complications, and mortality risks, thus offering precise stratification and resource optimization. Specific applications include AI-assisted anemia management, penicillin allergy delabelling, and opioid use prediction after major surgeries. AI also enhances patient education through tools like ChatGPT and improves smoking cessation efforts using conversational AI. NLP has demonstrated better accuracy than standard International Classification of Disease (ICD) codes in identifying risky alcohol use (>2 standard drinks per day before surgery). However, barriers to widespread adoption include data privacy, algorithmic bias, and clinician skepticism. Future studies should focus on validating AI models across diverse populations and integrating AI recommendations within clinical workflows while adhering to evolving regulatory standards. Ultimately, AI's incorporation into preoperative assessments could significantly boost the efficiency and impact of clinic resources, potentially shifting outcomes more favorably by improving the slope of resource utilization versus patient outcome curves. Continued research and refinement are essential for AI to achieve its full potential in perioperative medicine.
(Copyright © 2025 International Anesthesia Research Society.)*
*The authors declare no conflicts of interest.*