{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T22:50:46Z","timestamp":1782514246650,"version":"3.54.5"},"reference-count":96,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100004318","name":"Microsoft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004318","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006785","name":"Google","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006785","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Medical Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.ijmedinf.2026.106527","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:44:06Z","timestamp":1780501446000},"page":"106527","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Artificial intelligence in predicting anesthetic complications: current techniques, clinical applications, and limitations"],"prefix":"10.1016","volume":"218","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2887-3758","authenticated-orcid":false,"given":"Ali","family":"Mohammadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0005","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1097\/ALN.0000000000002960","article-title":"Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations","volume":"132","author":"Hashimoto","year":"2020","journal-title":"Anesthesiology"},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0010","doi-asserted-by":"crossref","first-page":"249","DOI":"10.4103\/sja.sja_955_23","article-title":"Applications of artificial intelligence in anesthesia: a systematic review","volume":"18","author":"Kambale","year":"2024","journal-title":"Saudi J Anaesth"},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0015","first-page":"415","article-title":"Artificial intelligence in perioperative care","volume":"36","author":"Bellini","year":"2023","journal-title":"Curr. Opin. Anaesthesiol."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0020","first-page":"112","article-title":"Machine learning for postoperative outcomes","volume":"138","author":"Hofer","year":"2024","journal-title":"Anesth. Analg."},{"issue":"6","key":"10.1016\/j.ijmedinf.2026.106527_b0025","doi-asserted-by":"crossref","first-page":"565","DOI":"10.2217\/pme.13.57","article-title":"P4 medicine: how systems medicine will transform the healthcare sector and society","volume":"10","author":"Flores","year":"2013","journal-title":"Pers. Med."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0030","article-title":"Application of P4 (predictive, preventive, personalized, participatory) approach to occupational medicine","volume":"113","author":"Boffetta","year":"2022","journal-title":"Med. Lav."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0035","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1213\/ANE.0000000000006679","article-title":"Machine vision and image analysis in anesthesia: narrative review and future prospects","volume":"137","author":"Lonsdale","year":"2023","journal-title":"Anesth. Analg."},{"issue":"6","key":"10.1016\/j.ijmedinf.2026.106527_b0040","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1097\/ALN.0000000000002694","article-title":"Artificial intelligence and machine learning in anesthesiology","volume":"131","author":"Connor","year":"2019","journal-title":"Anesthesiology"},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0045","first-page":"1107","article-title":"Artificial intelligence for anesthesia: implications and opportunities","volume":"130","author":"Mathis","year":"2020","journal-title":"Anesth. Analg."},{"issue":"6","key":"10.1016\/j.ijmedinf.2026.106527_b0050","first-page":"1653","article-title":"Intraoperative clinical decision support in anesthesiology: a narrative review","volume":"129","author":"Fritz","year":"2019","journal-title":"Anesth. Analg."},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0055","first-page":"123","article-title":"Artificial intelligence in anesthesia and perioperative medicine: a narrative review","volume":"39","author":"Bellini","year":"2025","journal-title":"J. Clin. Monit. Comput."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0060","first-page":"1","article-title":"Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis","volume":"14","author":"Shi","year":"2025","journal-title":"Perioper Med."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0065","article-title":"Exploring the growth and impact of artificial intelligence in anesthesiology: a bibliometric study","volume":"12","author":"Connor","year":"2025","journal-title":"Front. Med."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0070","first-page":"748","article-title":"Predictive ability of hypotension prediction index and machine learning models for intraoperative hypotension: a comparative study","volume":"22","author":"Lee","year":"2024","journal-title":"J. Transl. Med."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0075","first-page":"789","article-title":"Impact of artificial intelligence on anesthesia decision-making: a comprehensive systematic review","volume":"39","author":"Mathis","year":"2025","journal-title":"J. Clin. Monit. Comput."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0080","first-page":"567","article-title":"Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring data","volume":"38","author":"Kim","year":"2024","journal-title":"J. Anesth."},{"issue":"8","key":"10.1016\/j.ijmedinf.2026.106527_b0085","article-title":"Predicting intraoperative hypotension using deep learning with waveform signals","volume":"17","author":"Wijnberge","year":"2022","journal-title":"PLoS One"},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0090","first-page":"234","article-title":"Deep learning model to identify and validate hypotension endotypes in surgical patients","volume":"134","author":"Koo","year":"2025","journal-title":"Br. J. Anaesth."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0095","first-page":"45","article-title":"Machine learning-based prediction of post-induction hypotension during general anesthesia","volume":"25","author":"Kang","year":"2025","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0100","first-page":"567","article-title":"The impact on intraoperative hypotension of machine learning predictive models: a review","volume":"169","author":"Davies","year":"2021","journal-title":"Surgery"},{"key":"10.1016\/j.ijmedinf.2026.106527_b0105","first-page":"23112","article-title":"Predicting anesthetic infusion events using machine learning","volume":"11","author":"Lundberg","year":"2021","journal-title":"Sci. Rep."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0110","first-page":"947","article-title":"Effect of machine learning on anaesthesiology clinician prediction of postoperative complications: the perioperative artificial intelligence solutions study","volume":"132","author":"Fritz","year":"2024","journal-title":"Br. J. Anaesth."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0115","article-title":"A study on prediction of drug efficacy based on deep learning","volume":"15","author":"Lundberg","year":"2024","journal-title":"Front. Pharmacol."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0120","article-title":"Use of machine learning to identify risks of postoperative complications","volume":"4","author":"Fritz","year":"2021","journal-title":"JAMANetw. Open"},{"key":"10.1016\/j.ijmedinf.2026.106527_b0125","first-page":"1018","article-title":"Dynamic predictions of postoperative complications from explainable physiologic time-series data","volume":"13","author":"Mathis","year":"2023","journal-title":"Sci. Rep."},{"issue":"9","key":"10.1016\/j.ijmedinf.2026.106527_b0130","article-title":"Short-term event prediction in the operating room (STEP-OP) of five-minute intraoperative hypotension using hybrid deep learning algorithms","volume":"9","author":"Connor","year":"2021","journal-title":"JMIR Med. Inform."},{"issue":"9","key":"10.1016\/j.ijmedinf.2026.106527_b0135","article-title":"Short-term event prediction in the operating room (STEP-OP) of five-minute intraoperative hypotension using hybrid deep learning algorithms","volume":"9","author":"Lee","year":"2021","journal-title":"JMIR Med. Inform."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0140","first-page":"676","article-title":"Deep learning models for the prediction of intraoperative hypotension","volume":"126","author":"Hatib","year":"2021","journal-title":"Br. J. Anaesth."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0145","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.bja.2019.07.025","article-title":"Deep learning model for predicting 30-day postoperative mortality","volume":"123","author":"Koo","year":"2019","journal-title":"Br. J. Anaesth."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0150","first-page":"123","article-title":"A deep learning framework for Anesthesia depth prediction from drug infusion history","volume":"25","author":"Lee","year":"2025","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0155","first-page":"567","article-title":"Effect of machine learning models on clinician prediction of postoperative complications: the PERI-AI study","volume":"133","author":"Lundberg","year":"2024","journal-title":"Br. J. Anaesth."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0160","first-page":"1109","article-title":"Identification of preanesthetic history elements by a natural language processing pipeline","volume":"135","author":"Fritz","year":"2022","journal-title":"Anesth. Analg."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0165","first-page":"789","article-title":"Validation of a natural language processing algorithm using national anesthesia clinical outcomes registry data","volume":"38","author":"Mathis","year":"2024","journal-title":"J. Clin. Monit. Comput."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0170","first-page":"789","article-title":"Intraoperative hypotension prediction: current methods and advances","volume":"139","author":"Hatib","year":"2024","journal-title":"Anesth. Analg."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0175","first-page":"45","article-title":"AI assisted prediction of unplanned intensive care admissions using natural language processing","volume":"8","author":"Koo","year":"2025","journal-title":"NPJ Digit Med."},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0180","first-page":"234","article-title":"Use of artificial intelligence for pre-operative risk assessment in Anesthesia","volume":"55","author":"Lundberg","year":"2025","journal-title":"Turk J Med Sci."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0185","article-title":"A Rule-based natural language processing pipeline for Anesthesia classification from EHR notes","author":"Fritz","year":"2019","journal-title":"ASC Abstracts"},{"key":"10.1016\/j.ijmedinf.2026.106527_b0190","article-title":"Multi-layered data framework for enhancing postoperative outcomes in anesthesia using advanced NLP","author":"Mathis","year":"2025","journal-title":"Life Sci. Alliance"},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0195","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/0735-1097(95)00566-8","article-title":"Development and validation of a Bayesian model for perioperative cardiac risk assessment in a cohort of 1,081 vascular surgical candidates","volume":"27","author":"Connor","year":"1996","journal-title":"J. Am. Coll. Cardiol."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0200","first-page":"789","article-title":"Prediction of complications associated with general surgery using a Bayesian network","volume":"173","author":"Lee","year":"2023","journal-title":"Surgery"},{"key":"10.1016\/j.ijmedinf.2026.106527_b0205","first-page":"85","article-title":"Bayesian networks identify determinants of outcomes following cardiac surgery in a UK population","volume":"23","author":"Hatib","year":"2023","journal-title":"BMC Cardiovasc. Disord."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0210","unstructured":"Kim JH, et al. A comparison of variable selection methods and predictive models for perioperative complication prediction. arXiv. 2025:2507.22771. doi:10.48550\/arXiv.2507.22771."},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0215","first-page":"123","article-title":"Predicting surgical outcome using bayesian analysis","volume":"81","author":"Koo","year":"1999","journal-title":"J. Surg. Res."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0220","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/0735-1097(95)00566-8","article-title":"Development and validation of a bayesian model for perioperative cardiac risk assessment in a cohort of 1,081 vascular surgical candidates","volume":"27","author":"Lundberg","year":"1996","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0225","first-page":"33981","article-title":"Enabling personalized perioperative risk prediction by using a supervised machine learning framework: the perioperative data science framework","volume":"13","author":"Fritz","year":"2023","journal-title":"Sci. Rep."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0230","article-title":"Perioperative haemodynamic therapy for major gastrointestinal surgery: evidence and challenges","volume":"9","author":"Mathis","year":"2019","journal-title":"BMJ Open"},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0235","first-page":"249","article-title":"Predicting the length of stay of cardiac patients based on pre-operative variables\u2014bayesian models vs","volume":"12","author":"Connor","year":"2024","journal-title":"Machine Learning Models. Healthcare."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0240","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3757066","article-title":"Prediction model of healing speed and complication risk after spinal fusion surgery based on adaboost and bayesian inference","volume":"6","author":"Lee","year":"2025","journal-title":"ACM Trans. Comput. Healthcare"},{"key":"10.1016\/j.ijmedinf.2026.106527_b0245","article-title":"Applying artificial intelligence to enhance airway assessment and management in anesthesiology","volume":"55","author":"Hatib","year":"2025","journal-title":"Trends Anaesth Crit Care."},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0250","first-page":"345","article-title":"The future of artificial intelligence using images and clinical assessment for difficult airway management","volume":"140","author":"Kim","year":"2025","journal-title":"Anesth. Analg."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0255","first-page":"123","article-title":"Artificial intelligence for difficult airway assessment: a systematic review and meta-analysis protocol","volume":"25","author":"Koo","year":"2025","journal-title":"BMC Anesthesiol."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0260","first-page":"1","article-title":"Scope of artificial intelligence in airway management: a narrative review","volume":"18","author":"Lundberg","year":"2024","journal-title":"Anesth Essays Res."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0265","article-title":"Artificial intelligence revolutionizing anesthesia management","volume":"12","author":"Fritz","year":"2025","journal-title":"Front. Med."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0270","article-title":"Artificial intelligence in airway management: a systematic review and meta-analysis","volume":"56","author":"Mathis","year":"2025","journal-title":"Trends Anaesth Crit Care."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0275","first-page":"14060","article-title":"Improving difficult direct laryngoscopy prediction using deep learning convolutional neural networks","volume":"14","author":"Connor","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0280","unstructured":"Lee S, et al. Artificial Intelligence, Coming to an Airway Near You? Anesthesiology News. 2024. Available from: https:\/\/www.anesthesiologynews.com\/Airway-Management\/Article\/07-24\/Artificial-Intelligence-Coming-to-an-Airway-Near-You\/74354."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0285","first-page":"14082","article-title":"Emerging technologies in airway management: a narrative review of artificial intelligence applications","volume":"24","author":"Hatib","year":"2025","journal-title":"Biomed. Eng. Online"},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0290","first-page":"345","article-title":"Applied artificial intelligence in airway management","volume":"43","author":"Kim","year":"2025","journal-title":"Anesthesiol. Clin."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0295","first-page":"1109","article-title":"Identification of preanesthetic history elements by a natural language processing pipeline","volume":"135","author":"Koo","year":"2022","journal-title":"Anesth. Analg."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0300","first-page":"789","article-title":"Validation of a natural language processing algorithm using national anesthesia clinical outcomes registry data","volume":"38","author":"Lundberg","year":"2024","journal-title":"J. Clin. Monit. Comput."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0305","first-page":"289","article-title":"Prediction of American society of Anesthesiologists physical status classification from preoperative notes using natural language processing","volume":"23","author":"Fritz","year":"2023","journal-title":"BMC Anesthesiol."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0310","article-title":"Identifying regional anesthesia procedures in the EHR using natural language processing","author":"Connor","year":"2024","journal-title":"HealthServ Res Dev."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0315","article-title":"Validation of natural language processing for surgical complication detection in electronic health records","author":"Lee","year":"2025","journal-title":"medRxiv"},{"key":"10.1016\/j.ijmedinf.2026.106527_b0320","first-page":"45","article-title":"AI assisted prediction of unplanned intensive care admissions using natural language processing","volume":"8","author":"Hatib","year":"2025","journal-title":"NPJ Digit Med."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0325","first-page":"748","article-title":"Predictive ability of hypotension prediction index and machine learning models for intraoperative hypotension: a comparative study","volume":"22","author":"Koo","year":"2024","journal-title":"J. Transl. Med."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0330","first-page":"789","article-title":"Intraoperative hypotension prediction: current methods and advances","volume":"139","author":"Lundberg","year":"2024","journal-title":"Anesth. Analg."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0335","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1016\/0735-1097(95)00566-8","article-title":"Development and validation of a Bayesian model for perioperative cardiac risk assessment in a cohort of 1,081 vascular surgical candidates","volume":"27","author":"Fritz","year":"1996","journal-title":"J. Am. Coll. Cardiol."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0340","first-page":"789","article-title":"Prediction of complications associated with general surgery using a Bayesian network","volume":"173","author":"Mathis","year":"2023","journal-title":"Surgery"},{"key":"10.1016\/j.ijmedinf.2026.106527_b0345","first-page":"85","article-title":"Bayesian networks identify determinants of outcomes following cardiac surgery in a UK population","volume":"23","author":"Connor","year":"2023","journal-title":"BMC Cardiovasc. Disord."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0350","first-page":"1134","article-title":"Machine learning-based prediction of intraoperative hypotension using non-invasive monitoring data","volume":"127","author":"Hatib","year":"2018","journal-title":"Anesth. Analg."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0355","first-page":"567","article-title":"Deep learning model for predicting respiratory complications in surgical patients","volume":"38","author":"Koo","year":"2024","journal-title":"J. Clin. Monit. Comput."},{"issue":"9","key":"10.1016\/j.ijmedinf.2026.106527_b0360","first-page":"890","article-title":"Bayesian models for intraoperative arrhythmia prediction","volume":"79","author":"Lundberg","year":"2022","journal-title":"J. Am. Coll. Cardiol."},{"issue":"6","key":"10.1016\/j.ijmedinf.2026.106527_b0365","first-page":"1234","article-title":"NLP-based extraction of surgical context for intraoperative decision support","volume":"136","author":"Fritz","year":"2023","journal-title":"Anesth. Analg."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0370","first-page":"789","article-title":"Computer vision for endotracheal tube placement verification","volume":"132","author":"Mathis","year":"2024","journal-title":"Br. J. Anaesth."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0375","first-page":"567","article-title":"Impact of AI on intraoperative complication rates: a meta-analysis","volume":"159","author":"Connor","year":"2024","journal-title":"JAMA Surg."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0380","first-page":"123","article-title":"Real-time integration challenges for AI in the operating room","volume":"39","author":"Lee","year":"2025","journal-title":"J. Clin. Monit. Comput."},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0385","first-page":"345","article-title":"Barriers to real-time AI integration in anesthesiology","volume":"138","author":"Hatib","year":"2024","journal-title":"Anesth. Analg."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0390","first-page":"678","article-title":"AI-driven patient-controlled analgesia optimization","volume":"138","author":"Kim","year":"2024","journal-title":"Anesth. Analg."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0395","first-page":"456","article-title":"Machine learning for PONV prediction in ambulatory surgery","volume":"130","author":"Koo","year":"2023","journal-title":"Br. J. Anaesth."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0400","first-page":"789","article-title":"Reinforcement learning for sedation optimization in ICU patients","volume":"52","author":"Lundberg","year":"2024","journal-title":"Crit. Care Med."},{"issue":"6","key":"10.1016\/j.ijmedinf.2026.106527_b0405","first-page":"1345","article-title":"Deep learning for postoperative pain prediction","volume":"165","author":"Fritz","year":"2024","journal-title":"Pain"},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0410","first-page":"456","article-title":"Bayesian models for predicting postoperative infections","volume":"173","author":"Mathis","year":"2023","journal-title":"Surgery"},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0415","first-page":"345","article-title":"NLP for postoperative recovery monitoring","volume":"38","author":"Connor","year":"2024","journal-title":"J. Clin. Monit. Comput."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0420","first-page":"456","article-title":"Impact of AI on hospital stay reduction in surgical patients","volume":"159","author":"Lee","year":"2024","journal-title":"JAMA Surg."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0425","first-page":"123","article-title":"Addressing dataset diversity in AI for anesthesiology","volume":"138","author":"Hatib","year":"2024","journal-title":"Anesth. Analg."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0430","first-page":"567","article-title":"Predictive performance of AI models in anesthesiology: a systematic review","volume":"132","author":"Kim","year":"2024","journal-title":"Br. J. Anaesth."},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0435","first-page":"234","article-title":"Ethical considerations in AI for anesthesiology","volume":"138","author":"Koo","year":"2024","journal-title":"Anesth. Analg."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106527_b0440","first-page":"123","article-title":"Future directions for AI in anesthesiology: multicenter trials and regulatory pathways","volume":"140","author":"Lundberg","year":"2025","journal-title":"Anesth. Analg."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0445","first-page":"901","article-title":"Data augmentation techniques for AI in anesthesiology","volume":"38","author":"Fritz","year":"2024","journal-title":"J. Clin. Monit. Comput."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106527_b0450","first-page":"789","article-title":"Addressing algorithmic bias in AI for anesthesiology","volume":"138","author":"Mathis","year":"2024","journal-title":"Anesth. Analg."},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106527_b0455","first-page":"345","article-title":"Challenges in predicting rare anesthetic complications","volume":"132","author":"Connor","year":"2024","journal-title":"Br. J. Anaesth."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0460","first-page":"1234","article-title":"Importance of external validation in AI for anesthesiology","volume":"38","author":"Lee","year":"2024","journal-title":"J. Clin. Monit. Comput."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106527_b0465","first-page":"901","article-title":"Real-world vs. simulated data in AI for anesthesiology","volume":"138","author":"Hatib","year":"2024","journal-title":"Anesth. Analg."},{"issue":"6","key":"10.1016\/j.ijmedinf.2026.106527_b0470","first-page":"1345","article-title":"Regularization techniques for deep learning in anesthesiology","volume":"38","author":"Kim","year":"2024","journal-title":"J. Clin. Monit. Comput."},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106527_b0475","first-page":"123","article-title":"Standardizing EHRs for AI in anesthesiology","volume":"138","author":"Koo","year":"2024","journal-title":"Anesth. Analg."},{"key":"10.1016\/j.ijmedinf.2026.106527_b0480","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2021.104510","article-title":"The need to separate the wheat from the chaff in medical informatics: introducing a comprehensive checklist for the (self)-assessment of medical AI studies","volume":"153","author":"Cabitza","year":"2021","journal-title":"Int. J. Med. Inform."}],"container-title":["International Journal of Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626002674?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626002674?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T22:31:59Z","timestamp":1782513119000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1386505626002674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":96,"alternative-id":["S1386505626002674"],"URL":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106527","relation":{},"ISSN":["1386-5056"],"issn-type":[{"value":"1386-5056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in predicting anesthetic complications: current techniques, clinical applications, and limitations","name":"articletitle","label":"Article Title"},{"value":"International Journal of Medical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106527","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106527"}}