{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:36:50Z","timestamp":1790199410670,"version":"4.1.0"},"reference-count":85,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR AI"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The impact of surgical complications is substantial and multifaceted, affecting patients and their families, surgeons, and health care systems. Despite the remarkable progress in artificial intelligence (AI), there remains a notable gap in the prospective implementation of AI models in surgery that use real-time data to support decision-making and enable proactive intervention to reduce the risk of surgical complications.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This scoping review aims to assess and analyze the adoption and use of AI models for preventing surgical complications. Furthermore, this review aims to identify barriers and facilitators for implementation at the bedside.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, we conducted a literature search using IEEE Xplore, Scopus, Web of Science, MEDLINE, ProQuest, PubMed, ABI, Embase, Epistemonikos, CINAHL, and Cochrane registries. The inclusion criteria included empirical, peer-reviewed studies published in English between January 2013 and January 2025, involving AI models for preventing surgical complications (surgical site infections, and heart and lung complications or stroke) in real-world settings. Exclusions included retrospective algorithm-only validations, nonempirical research (eg, editorials or protocols), and non-English studies. Study characteristics and AI model development details were extracted, along with performance statistics (eg, sensitivity and area under the receiver operating characteristic curve). We then used thematic analysis to synthesize findings related to AI models, prediction outputs, and validation methods. Studies were grouped into three main themes: (1) duration of hypotension, (2) risk for complications, and (3) decision support tool.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Of the 275 identified records, 19 were included. The included models frequently demonstrated strong technical accuracy with high sensitivity and area under the receiver operating characteristic curve, particularly among studies evaluating decision support tools. However, only a few models were adopted routinely in clinical practice. Two studies evaluated the clinicians\u2019 perceptions regarding the use of AI models, reporting predominantly positive assessments of their usefulness.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Overall, AI models hold potential to predict and prevent surgical complications as the validation studies demonstrated high accuracy. However, implementation in routine practice remains limited by usability barriers, workflow misalignment, trust concerns, and financial and ethical constraints. The evidence included in this scoping review was limited by the heterogeneity in study design and the predominance of small-scale feasibility studies, particularly for hypotension prediction. Future research should prioritize prospectively validated models that use other physiologic features and address clinicians\u2019 concerns regarding generalizability and adoption.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/75064","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T13:55:14Z","timestamp":1769090114000},"page":"e75064-e75064","source":"Crossref","is-referenced-by-count":5,"title":["Application of AI Models for Preventing Surgical Complications: Scoping Review of Clinical Readiness and Barriers to Implementation"],"prefix":"10.2196","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0692-7240","authenticated-orcid":false,"given":"Kjersti","family":"Mevik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9464-3407","authenticated-orcid":false,"given":"Ashenafi Zebene","family":"Woldaregay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4267-9363","authenticated-orcid":false,"given":"Eva Lindell","family":"Jonsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7696-1272","authenticated-orcid":false,"given":"Miguel","family":"Tejedor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1199-6590","authenticated-orcid":false,"given":"Claire","family":"Temple-Oberle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijsu.2020.07.017","article-title":"Trauma of major surgery: a global problem that is not going away","volume":"81","author":"Dobson","journal-title":"Int J Surg"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40140-021-00493-y","article-title":"The hidden pandemic: the cost of postoperative complications","volume":"12","author":"Ludbrook","journal-title":"Curr Anesthesiol Rep"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.amsu.2021.102530","article-title":"The psychosocial impact of surgical complications on the operating surgeon: a scoping review","volume":"67","author":"Siddaiah-Subramanya","journal-title":"Ann Med Surg (Lond)"},{"issue":"12","key":"R4","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1111\/medu.12058","article-title":"Waking up the next morning: surgeons\u2019 emotional reactions to adverse events","volume":"46","author":"Luu","journal-title":"Med Educ"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjsurg.2023.09.045","article-title":"Scoping review of the second victim syndrome among surgeons: understanding the impact, responses, and support systems","volume":"229","author":"Chong","journal-title":"Am J Surg"},{"issue":"3","key":"R6","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1001\/jama.2022.24075","article-title":"Surgical site infection prevention: a review","volume":"329","author":"Seidelman","journal-title":"JAMA"},{"issue":"12","key":"R7","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.50942","article-title":"Challenges and solutions in postoperative complications: a narrative review in general surgery","volume":"15","author":"Javed","journal-title":"Cureus"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.869607","article-title":"Guiding principles for surgical pathways: a tool for improving outcomes and patient safety","volume":"10","author":"Bolcato","journal-title":"Front Public Health"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1177\/00031348221101488","article-title":"Artificial intelligence and machine learning in prediction of surgical complications: current state, applications, and implications","volume":"89","author":"Hassan","journal-title":"Am Surg"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000006577","article-title":"Artificial intelligence assisted surgical scene recognition: a comparative study amongst healthcare professionals","author":"Williams","journal-title":"Ann Surg"},{"issue":"12","key":"R11","doi-asserted-by":"publisher","first-page":"7816","DOI":"10.1097\/JS9.0000000000001798","article-title":"Impact of an AI-based laparoscopic cholecystectomy coaching program on the surgical performance: a randomized controlled trial","volume":"110","author":"Wu","journal-title":"Int J Surg"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.1159\/000536393","article-title":"Artificial intelligence in surgery: the future is now","author":"Guni","journal-title":"Eur Surg Res"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1097\/SLA.0000000000005853","article-title":"Artificial intelligence\u2013enabled decision support in surgery: state-of-the-art and future directions","volume":"278","author":"Loftus","journal-title":"Ann Surg"},{"issue":"4","key":"R14","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1213\/ANE.0000000000003718","article-title":"Use of the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator during preoperative risk discussion: the patient perspective","volume":"128","author":"Raymond","journal-title":"Anesth Analg"},{"issue":"5","key":"R15","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1016\/j.jamcollsurg.2013.07.385","article-title":"Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons","volume":"217","author":"Bilimoria","journal-title":"J Am Coll Surg"},{"issue":"4","key":"R16","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1161\/CIRCULATIONAHA.110.015701","article-title":"Development and validation of a risk calculator for prediction of cardiac risk after surgery","volume":"124","author":"Gupta","journal-title":"Circulation"},{"issue":"4","key":"R17","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1097\/SLA.0000000000002956","article-title":"Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning\u2014based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator","volume":"268","author":"Bertsimas","journal-title":"Ann Surg"},{"issue":"1","key":"R18","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.suc.2020.08.012","article-title":"Evolution of risk calculators and the dawn of artificial intelligence in predicting patient complications","volume":"101","author":"Podrat","journal-title":"Surg Clin North Am"},{"issue":"1","key":"R19","doi-asserted-by":"publisher","DOI":"10.1111\/iwj.70280","article-title":"Risk predictions of surgical wound complications based on a machine learning algorithm: a systematic review","volume":"21","author":"Zhang","journal-title":"Int Wound J"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105370","article-title":"Exploring surgical infection prediction: a comparative study of established risk indexes and a novel model","volume":"184","author":"Mevik","journal-title":"Int J Med Inform"},{"issue":"6","key":"R21","doi-asserted-by":"publisher","DOI":"10.2196\/18186","article-title":"Artificial intelligence\u2013based multimodal risk assessment model for surgical site infection (AMRAMS): development and validation study","volume":"8","author":"Chen","journal-title":"JMIR Med Inform"},{"issue":"8","key":"R22","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1016\/j.jiph.2020.06.006","article-title":"Artificial intelligence\u2013based tools to control healthcare associated infections: a systematic review of the literature","volume":"13","author":"Scardoni","journal-title":"J Infect Public Health"},{"issue":"4","key":"R23","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1053\/j.semvascsurg.2021.10.008","article-title":"Current applications of artificial intelligence in vascular surgery","volume":"34","author":"Fischer","journal-title":"Semin Vasc Surg"},{"issue":"2","key":"R24","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1097\/XCS.0000000000000242","article-title":"Artificial intelligence and surgery: ethical dilemmas and open issues","volume":"235","author":"Cobianchi","journal-title":"J Am Coll Surg"},{"issue":"1","key":"R25","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.51631","article-title":"Future of artificial intelligence in surgery: a narrative review","volume":"16","author":"Amin","journal-title":"Cureus"},{"issue":"5","key":"R26","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1038\/s41591-024-02970-3","article-title":"Artificial intelligence in surgery","volume":"30","author":"Varghese","journal-title":"Nat Med"},{"key":"R27","doi-asserted-by":"publisher","DOI":"10.3389\/fsurg.2024.1393898","article-title":"Current and future applications of artificial intelligence in surgery: implications for clinical practice and research","volume":"11","author":"Morris","journal-title":"Front Surg"},{"key":"R28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2020.03.016","article-title":"Reporting scoping reviews\u2014PRISMA ScR extension","volume":"123","author":"McGowan","journal-title":"J Clin Epidemiol"},{"issue":"12","key":"R29","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1016\/j.jclinepi.2014.03.013","article-title":"Scoping reviews: time for clarity in definition, methods, and reporting","volume":"67","author":"Colquhoun","journal-title":"J Clin Epidemiol"},{"issue":"4","key":"R30","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1111\/jan.14743","article-title":"Undertaking a scoping review: a practical guide for nursing and midwifery students, clinicians, researchers, and academics","volume":"77","author":"Pollock","journal-title":"J Adv Nurs"},{"issue":"7","key":"R31","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-048008","article-title":"Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence","volume":"11","author":"Collins","journal-title":"BMJ Open"},{"key":"R32","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","journal-title":"BMJ"},{"key":"R33","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","journal-title":"BMJ"},{"issue":"2","key":"R34","doi-asserted-by":"publisher","DOI":"10.1002\/cl2.1230","article-title":": An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis","volume":"18","author":"Haddaway","journal-title":"Campbell Syst Rev"},{"issue":"11","key":"R35","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1001\/jama.2020.0592","article-title":"Effect of a machine learning\u2013derived 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","volume":"323","author":"Wijnberge","journal-title":"JAMA"},{"key":"R36","doi-asserted-by":"publisher","DOI":"10.3389\/fanes.2023.1193886","article-title":"Intraoperative hemodynamic optimization using the hypotension prediction index vs. goal-directed hemodynamic therapy during elective major abdominal surgery: the Predict-H multicenter randomized controlled trial","volume":"2","author":"Lorente","journal-title":"Front Anesthesiol"},{"issue":"6","key":"R37","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1007\/s10877-019-00433-6","article-title":"Hypotension Prediction Index based protocolized haemodynamic management reduces the incidence and duration of intraoperative hypotension in primary total hip arthroplasty: a single centre feasibility randomised blinded prospective interventional trial","volume":"34","author":"Schneck","journal-title":"J Clin Monit Comput"},{"issue":"1","key":"R38","doi-asserted-by":"publisher","DOI":"10.1186\/s13741-024-00369-9","article-title":"AcumenTM hypotension prediction index guidance for prevention and treatment of hypotension in noncardiac surgery: a prospective, single-arm, multicenter trial","volume":"13","author":"Bao","journal-title":"Perioper Med (Lond)"},{"issue":"24","key":"R39","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10245884","article-title":"The use of the Hypotension Prediction Index integrated in an algorithm of goal directed hemodynamic treatment during moderate and high-risk surgery","volume":"10","author":"Tsoumpa","journal-title":"J Clin Med"},{"issue":"2","key":"R40","doi-asserted-by":"publisher","DOI":"10.3390\/jpm14020211","article-title":"Individualized perioperative hemodynamic management using Hypotension Prediction Index software and the dynamics of troponin and NTproBNP concentration changes in patients undergoing oncological abdominal surgery","volume":"14","author":"Cylwik","journal-title":"J Pers Med"},{"issue":"2","key":"R41","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11020392","article-title":"Proactive management of intraoperative hypotension reduces biomarkers of organ injury and oxidative stress during elective non-cardiac surgery: a pilot randomized controlled trial","volume":"11","author":"Murabito","journal-title":"J Clin Med"},{"issue":"1","key":"R42","doi-asserted-by":"publisher","DOI":"10.1186\/s12871-023-02069-1","article-title":"Hypotension Prediction Index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial","volume":"23","author":"\u0160ribar","journal-title":"BMC Anesthesiol"},{"issue":"6","key":"R43","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1097\/ALN.0000000000003557","article-title":"Hypotension Prediction Index for prevention of hypotension during moderate- to high-risk noncardiac surgery","volume":"133","author":"Maheshwari","journal-title":"Anesthesiology"},{"issue":"14","key":"R44","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12144571","article-title":"The impact of intraoperative haemodynamic monitoring, prediction of hypotension and goal-directed therapy on the outcomes of patients treated with posterior fusion due to adolescent idiopathic scoliosis","volume":"12","author":"Andrzejewska","journal-title":"J Clin Med"},{"key":"R45","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjao.2023.100140","article-title":"Intraoperative hypotension when using Hypotension Prediction Index software during major noncardiac surgery: a European multicentre prospective observational registry (EU HYPROTECT)","volume":"6","author":"Kouz","journal-title":"BJA Open"},{"issue":"5","key":"R46","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.11973","article-title":"Performance of a machine learning algorithm using electronic health record data to predict postoperative complications and report on a mobile platform","volume":"5","author":"Ren","journal-title":"JAMA Netw Open"},{"issue":"1","key":"R47","doi-asserted-by":"publisher","DOI":"10.1186\/s13037-020-00256-4","article-title":"The value of the \u201cSurgical Risk Preoperative Assessment System\u201d (SURPAS) in preoperative consultation for elective surgery: a pilot study","volume":"14","author":"Bronsert","journal-title":"Patient Saf Surg"},{"issue":"4","key":"R48","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1097\/TA.0000000000004030","article-title":"Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery","volume":"95","author":"El Moheb","journal-title":"J Trauma Acute Care Surg"},{"key":"R49","doi-asserted-by":"publisher","DOI":"10.2196\/39044","article-title":"Perioperative risk assessment of patients using the MyRISK digital score completed before the preanesthetic consultation: prospective observational study","volume":"6","author":"Ferr\u00e9","journal-title":"JMIR Perioper Med"},{"issue":"5","key":"R50","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1016\/j.bja.2024.08.004","article-title":"Effect of machine learning models on clinician prediction of postoperative complications: the perioperative ORACLE randomised clinical trial","volume":"133","author":"Fritz","journal-title":"Br J Anaesth"},{"issue":"16","key":"R51","doi-asserted-by":"publisher","DOI":"10.3390\/nu16162768","article-title":"Diagnosing sarcopenia with AI-aided ultrasound (DINOSAUR)\u2014a pilot study","volume":"16","author":"Yik","journal-title":"Nutrients"},{"issue":"6","key":"R52","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1016\/j.bja.2024.02.025","article-title":"Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications","volume":"132","author":"Li","journal-title":"Br J Anaesth"},{"issue":"4","key":"R53","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1097\/ALN.0000000000002300","article-title":"Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis","volume":"129","author":"Hatib","journal-title":"Anesthesiology"},{"issue":"4","key":"R54","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1097\/SLA.0000000000002706","article-title":"MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery","volume":"269","author":"Bihorac","journal-title":"Ann Surg"},{"key":"R55","unstructured":"Welcome to mysurgeryrisk calculator. URL: https:\/\/prismap.medicine.ufl.edu\/x\/com-dom-prisma-p.sites.medinfo.ufl.edu\/MySurgeryRisk.html [Accessed 12-02-2026]"},{"issue":"6","key":"R56","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.jamcollsurg.2009.01.043","article-title":"Development of an American College of Surgeons National Surgery Quality Improvement Program: morbidity and mortality risk calculator for colorectal surgery","volume":"208","author":"Cohen","journal-title":"J Am Coll Surg"},{"key":"R57","unstructured":"ACS NSQIP surgical risk calculator; version: 4.0.2. URL: https:\/\/riskcalculator.facs.org\/RiskCalculator\/ [Accessed 12-02-2026]"},{"issue":"1","key":"R58","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1097\/SLA.0000000000001678","article-title":"Surgical Risk Preoperative Assessment System (SURPAS): III. Accurate preoperative prediction of 8 adverse outcomes using 8 predictor variables","volume":"264","author":"Meguid","journal-title":"Ann Surg"},{"key":"R59","volume":"2019","author":"Cui","journal-title":"AMIA Annu Symp Proc"},{"key":"R60","unstructured":"Risk calculator. URL: https:\/\/aorm.wchscu.cn\/ [Accessed 12-02-2026]"},{"issue":"1","key":"R61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44254-025-00091-9","article-title":"Anesthesia transformed: AI pioneering a new era in perioperative medicine","volume":"3","author":"Xu","journal-title":"Anesthesiol Perioper Sci"},{"issue":"8","key":"R62","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0305949","article-title":"A comprehensive overview of barriers and strategies for AI implementation in healthcare: mixed-method design","volume":"19","author":"Nair","journal-title":"PLoS One"},{"key":"R63","doi-asserted-by":"publisher","DOI":"10.21037\/jmai-22-71","article-title":"Implementing artificial intelligence in clinical practice: a mixed-method study of barriers and facilitators","volume":"5","author":"Schouten","journal-title":"J Med Artif Intell"},{"key":"R64","unstructured":"The augmentOR\u2122 Portal: Empowering Surgeons with Surgical Insights. Asensus Surgical. 2024. URL: https:\/\/www.asensus.com\/documents\/augmentortm-portal-empowering-surgeons-surgical-insights [Accessed 30-01-2026]"},{"issue":"1","key":"R65","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1097\/SLA.0000000000002693","article-title":"Artificial intelligence in surgery: promises and perils","volume":"268","author":"Hashimoto","journal-title":"Ann Surg"},{"issue":"5","key":"R66","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1016\/j.surg.2018.07.002","article-title":"Eye of the beholder: risk calculators and barriers to adoption in surgical trainees","volume":"164","author":"Leeds","journal-title":"Surgery"},{"issue":"10","key":"R67","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.46454","article-title":"A systematic review of the barriers to the implementation of artificial intelligence in healthcare","volume":"15","author":"Ahmed","journal-title":"Cureus"},{"key":"R68","doi-asserted-by":"publisher","DOI":"10.2196\/32962","article-title":"The gap between AI and bedside: participatory workshop on the barriers to the integration, translation, and adoption of digital health care and AI startup technology into clinical practice","volume":"25","author":"Olaye","journal-title":"J Med Internet Res"},{"key":"R69","unstructured":"EU AI act: first regulation on artificial intelligence. European Parliament. URL: https:\/\/www.europarl.europa.eu\/topics\/en\/article\/20230601STO93804\/eu-ai-act-first-regulation-on-artificial-intelligence [Accessed 30-01-2026]"},{"issue":"6","key":"R70","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1007\/s10877-020-00465-3","article-title":"Hypotension Prediction Index: from proof-of-concept to proof-of-feasibility","volume":"34","author":"de Keijzer","journal-title":"J Clin Monit Comput"},{"issue":"1","key":"R71","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0279088","article-title":"Expectations and attitudes towards medical artificial intelligence: a qualitative study in the field of stroke","volume":"18","author":"Amann","journal-title":"PLoS One"},{"issue":"16","key":"R72","doi-asserted-by":"publisher","DOI":"10.3390\/s21165526","article-title":"Artificial intelligence surgery: How do we get to autonomous actions in surgery?","volume":"21","author":"Gumbs","journal-title":"Sensors (Basel)"},{"issue":"1","key":"R73","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00852-5","article-title":"An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals","volume":"6","author":"Lambert","journal-title":"NPJ Digit Med"},{"issue":"12","key":"R74","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10122493","article-title":"Economics of artificial intelligence in healthcare: diagnosis vs. treatment","volume":"10","author":"Khanna","journal-title":"Healthcare (Basel)"},{"issue":"16","key":"R75","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11162308","article-title":"Clinicians\u2019 perceptions of artificial intelligence: focus on workload, risk, trust, clinical decision making, and clinical integration","volume":"11","author":"Shamszare","journal-title":"Healthcare (Basel)"},{"issue":"3","key":"R76","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1007\/s43441-024-00628-3","article-title":"The evolving regulatory paradigm of AI in MedTech: a review of perspectives and where we are today","volume":"58","author":"Zhou","journal-title":"Ther Innov Regul Sci"},{"issue":"7","key":"R77","doi-asserted-by":"publisher","first-page":"724","DOI":"10.23736\/S0375-9393.18.12620-4","article-title":"Discrimination and calibration properties of the hypotension probability indicator during cardiac and vascular surgery","volume":"85","author":"Ranucci","journal-title":"Minerva Anestesiol"},{"issue":"2","key":"R78","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1213\/ANE.0000000000004121","article-title":"Ability of an arterial waveform analysis\u2013derived Hypotension Prediction Index to predict future hypotensive events in surgical patients","volume":"130","author":"Davies","journal-title":"Anesth Analg"},{"issue":"10","key":"R79","doi-asserted-by":"publisher","first-page":"1404","DOI":"10.1111\/aas.13964","article-title":"Hemodynamic monitoring with Hypotension Prediction Index versus arterial waveform analysis alone and incidence of perioperative hypotension","volume":"65","author":"Grundmann","journal-title":"Acta Anaesthesiol Scand"},{"issue":"1","key":"R80","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1097\/ALN.0000000000001432","article-title":"Relationship between intraoperative hypotension, defined by either reduction from baseline or absolute thresholds, and acute kidney and myocardial injury after noncardiac surgery: a retrospective cohort analysis","volume":"126","author":"Salmasi","journal-title":"Anesthesiology"},{"issue":"1","key":"R81","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s10877-022-00881-7","article-title":"Real-world outcomes of the hypotension prediction index in the management of intraoperative hypotension during non-cardiac surgery: a retrospective clinical study","volume":"37","author":"Solares","journal-title":"J Clin Monit Comput"},{"key":"R82","doi-asserted-by":"publisher","DOI":"10.1016\/j.xjon.2023.06.023","article-title":"Advanced artificial intelligence\u2013guided hemodynamic management within cardiac enhanced recovery after surgery pathways: a multi-institution review","volume":"16","author":"Reddy","journal-title":"JTCVS Open"},{"issue":"19","key":"R83","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11195585","article-title":"Hypotension Prediction Index software to prevent intraoperative hypotension during major non-cardiac surgery: protocol for a European multicenter prospective observational registry (EU-HYPROTECT)","volume":"11","author":"Monge Garc\u00eda","journal-title":"J Clin Med"},{"issue":"3","key":"R84","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1111\/bcp.15942","article-title":"Effect of machine learning\u2013guided haemodynamic optimization on postoperative free flap perfusion in reconstructive maxillofacial surgery: a study protocol","volume":"90","author":"Pra\u017eetina","journal-title":"Br J Clin Pharmacol"},{"issue":"1","key":"R85","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","journal-title":"BMC Med"}],"container-title":["JMIR AI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ai.jmir.org\/2026\/1\/e75064","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:05:56Z","timestamp":1782842756000},"score":1,"resource":{"primary":{"URL":"https:\/\/ai.jmir.org\/2026\/1\/e75064"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,17]]},"references-count":85,"URL":"https:\/\/doi.org\/10.2196\/75064","relation":{},"ISSN":["2817-1705"],"issn-type":[{"value":"2817-1705","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,17]]},"article-number":"v5i1e75064"}}