Günübirlik Anestezi Uygulamalarında Yapay Zeka

Pardo E, Le Cam E, Verdonk F. Artificial intelligence and nonoperating room anesthesia. Curr Opin Anaesthesiol. 2024;37(4):413–20.

Chang B, Kaye AD, Diaz JH, Westlake B, Dutton RP, Urman RD. Interventional procedures outside of the operating room: results from the national anesthesia clinical outcomes registry. J Patient Saf. 2018;14(1):9–16.

Nagrebetsky A, Gabriel RA, Dutton RP, Urman RD. Growth of nonoperating room anesthesia care in the united states: a contemporary trends analysis. Anesth Analg. 2017;124(4):1261–7.

Statement on Nonoperating Room Anesthesia Services. 2026. Available from: https://www.asahq.org/standards-and-practice-parameters/statement-on-nonoperating-room-anesthesia-services

Türk Anesteziyoloji ve Reanimasyon Derneği (TARD). Ameliyathane Dışı Anestezi Uygulamaları Kılavuzu. TARD Akademi; 2022. https://akademi.tard.org.tr

Kovacheva V, Nagle B. Opportunities of AI-powered applications in anesthesiology to enhance patient safety. Int Anesthesiol Clin. 2024;62(2):26–33.

Giri R, Firdhos S, Vida T. Artificial ıntelligence in anesthesia: enhancing precision, safety, and global access through data-driven systems. J Clin Med. 2025;14(19):6900.

American Society of Anesthesiologists (ASA). 2025. Artificial ıntelligence emerging as powerful patient safety tool in anesthesia. https://www.asahq.org/

What Is Artificial Intelligence? IBM. 2024. https://www.ibm.com/think/topics/artificial-intelligence

Joseph A, Lakshmi R. Use of artificial intelligence for preoperative anaesthesia evaluation - a systematic review. TPM. 2025;32(S3):370–7.

Fritz BA, Cui Z, Zhang M, He Y, Chen Y, Kronzer A, et al. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth. 2019;123(5):688–95.

Shi M, Huang Z, Xiao G, Xu B, Ren Q, Zhao H. Estimating the depth of anesthesia from EEG signals based on a deep residual shrinkage Network. Sensors. 2023;23(2):1008.

Shim J, Yoon W, Lee S, Chang S, Jung S, Chung J. Machine learning methods for the prediction of ıntraoperative hypotension with biosignal waveforms. Medicina (Mex). 2024;61(11):2039.

Yves D, Agarwal K, Chan J, Promoppatum P, Pattanasiricharoen A. Evaluating deep learning-based nerve segmentation in brachial plexus ultrasound under realistic data constraints. arXiv. 2026.

Lakhani P, Flanders A, Gorniak R. Endotracheal tube position assessment on chest radiographs using deep learning. Radiol Artif Intell. 2020;3(1):e200026.

Xu NY, Litake O, Tully JL, Meineke MN, Sinha A, Meyer M, et al. A pre-trained language model approach for triaging surgical patients for preoperative anesthesia clinics. J Clin Monit Comput. 2026;40(2):517-24.

Chung P, Fong CT, Walters AM, Yetisgen M, O’Reilly-Shah VN. Prediction of American Society of Anesthesiologists Physical Status Classification from preoperative clinical text narratives using natural language processing. BMC Anesthesiol. 2023;23(1):296.

Krenzer A, Banck M, Makowski K, Hekalo A, Fitting D, Troya J, et al. A real-time polyp-detection system with clinical application in colonoscopy using deep convolutional neural networks. J Imaging. 2023;9(2):26.

Jeffries SD, Pelletier ED, Song K, Tu Z, Sinha A, Hemmerling TM. Recognition of vocal cords during videolaryngoscopy based on state-of-the-art YOLO-V8 architecture. Anesth Analg. 2025;140(5):1227–9.

Eastwood P, Gilani SZ, McArdle N, Hillman D, Walsh J, Maddison K, et al. Predicting sleep apnea from three-dimensional face photography. J Clin Sleep Med. 2020;16(4):493-502.

Cascella M. The complex task of modelling artificial intelligence workflows for forecasting postoperative risk. J Anesth Analg Crit Care. 2025;5(1):82.

Syed S, Syed M, Prior F, Zozus M, Syeda HB, Greer ML, et al. Machine learning approach to optimize sedation use in endoscopic procedures. Stud Health Technol Inform. 2021;281:183–7.

Kaushikan MP, Muthukumar R, Balaji D, Rajasekaran S, Prabakaran S, Navin RBN, et al. Clinical questionnaire-based aı for obstructive sleep apnea risk prediction: a comparative analysis of machine learning models. Indian J Otolaryngol Head Neck Surg. 2026;78:2031-38.

Hayasaka T, Kawano K, Kurihara K, Suzuki H, Nakane M, Kawamae K. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care. 2021;9(1):38

Dost B, Turan Eİ, Aydın ME, Ahıskalıoğlu A, Narayanan M, Yılmaz R, et al. Artificial ıntelligence in anaesthesiology: current applications, challenges, and future directions. Turk J Anaesthesiol Reanim. 2025;53(6):282–92.

Choi HM, Kim Y, Kim J, Park J, Lee JH, Yoon YE, et al. Artificial intelligence-enhanced ECG score for perioperative risk assessment in non-cardiac surgery. Eur Heart J Digit Health. 2026;7(2):ztag006.

News A. When ıt comes to ASA physical status, anesthesiologists and aı agree. 2026. https://www.anesthesiologynews.com/Technology/Article/12-25/When-It-Comes-to-ASA-Physical-Status-Anesthesiologists-and-AI-Agree/79062

Introna M, Karippacheril JG, Pilla S, Trimarchi D, Gemma M, Martino D, et al. Artificial intelligence and EEG during anesthesia: ideal match or fleeting bond? Artif Intell Surg. 2026;6(1):1–17.

Li T, Huang Y, Wen P, Li Y. Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features. Brain Inform. 2024;11(1):28.

Park Y, Han SH, Byun W, Kim JH, Lee HC, Kim SJ. A Real-time depth of anesthesia monitoring system based on deep neural network with large EDO tolerant EEG analog front-end. IEEE Trans Biomed Circuits Syst. 2020;14(4):825–37.

Alsayed TK, Almalki RF, Aljumah MS, Habib FM, Alhumaidan IA, Almuteri TM, et al. Hybrid electroencephalogram-genomic deep learning for personalised depth of anaesthesia monitoring: a transformer-based depth of anaesthesia ındex calculator with real-time pharmacogenomic adaptation. J Adv Trends Med Res. 2025;2(3):573–80.

Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology. 2018;129(4):663–74.

Ripollés-Melchor J, Ruiz-Escobar A, Fernández-Valdes-Bango P, Lorente JV, Jiménez-López I, Abad-Gurumeta A, et al. Hypotension prediction index: from reactive to predictive hemodynamic management, the key to maintaining hemodynamic stability. Front Anesthesiol. 2023;2:1-16.

Sarhadi K, Hamman J, Avila J, Jian Z, Fleming NW. Hypotension prediction index: comparison between invasive and non-invasive pressure inputs. BMC Anesthesiol. 2025;25(1):221.

Valbuena-Bueno MA, Ripollés-Melchor J, Ruiz-Escobar A, Fernández-Valdes-Bango P, Lorente JV, Abad-Gurumeta A, et al. Hypotension prediction index decision support system: a new model for decision support in hemodynamic management. Front Anesthesiol. 2024;3:1-8.

Sriganesh K, Francis T, Mishra RK, Prasad NN, Chakrabarti D. Hypotension prediction index for minimising intraoperative hypotension: a systematic review and meta-analysis of randomised controlled trials. Indian J Anaesth. 2024;68(11):942–50.

Khanna AK, Bergese SD, Jungquist CR, Morimatsu H, Uezono S, Lee S, et al. Prediction of opioid-ınduced respiratory depression on ınpatient wards using continuous capnography and oximetry: an ınternational prospective, observational trial. Anesth Analg. 2020;131(4):1012–24.

Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2(10):749–60.

Park K, Kim NY, Kim KJ, Oh C, Chae D, Kim SY. A Simple risk scoring system for predicting the occurrence of aspiration pneumonia after gastric endoscopic submucosal dissection. Anesth Analg. 2022;134(1):114–22.

Gungor I, Gunaydin B, Buyukgebiz Yeşil BM, Bagcaz S, Ozdemir MG, Inan G, et al. Evaluation of the effectiveness of artificial intelligence for ultrasound guided peripheral nerve and plane blocks in recognizing anatomical structures. Ann Anat Anat Anz Off Organ Anat Ges. 2023;250:152143.

Bowness JS, Macfarlane AJR, Burckett-St Laurent D, Harris C, Margetts S, Morecroft M, et al. Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia. Br J Anaesth. 2023;130(2):226–33.

Jo Y, Baek S, Baek D, Oh C, Lee D, Hong B. Artificial intelligence in ultrasound-guided regional anesthesia: bridging the gap between potential and practice: a narrative review. Anesth Pain Med. 2025;20(4):357–70.

Desebbe O, Rinehart J, Van der Linden P, Cannesson M, Delannoy B, Vigneron M, et al. Control of postoperative hypotension using a closed-loop system for norepinephrine ınfusion in patients after cardiac surgery: a randomized trial. Anesth Analg. 2022;134(5):964–73.

Rinehart J, Desebbe O, Berna A, Lam I, Coeckelenbergh S, Cannesson M, et al. Systolic arterial pressure control using an automated closed-loop system for vasopressor ınfusion during ıntermediate-to-high-risk surgery: a feasibility study. J Pers Med. 2022;12(10):1554.

Artificial ıntelligence for smart procedural sedation in the gastrointestinal endoscopy süite. 2026. https://www.fortunejournals.com/articles/artificial-intelligence-for-smart-procedural-sedation-in-the-gastrointestinal-endoscopy-suite.html

Pasin L, Nardelli P, Pintaudi M, Greco M, Zambon M, Cabrini L, et al. Closed-Loop delivery systems versus manually controlled administration of total IV anesthesia: a meta-analysis of randomized clinical trials. Anesth Analg. 2017;124(2):456–64.

Xu C, Zhu Y, Wu L, Yu H, Liu J, Zhou F, et al. Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial. BMC Anesthesiol. 2022;22(1):313.

Woodward ZG, Urman RD, Domino KB. Safety of non-operating room anesthesia: a closed claims update. Anesthesiol Clin. 2017;35(4):569–81.

Nedoma J, Fajkus M, Martinek R, Nazeran H. Vital sign monitoring and cardiac triggering at 1.5 tesla: a practical solution by an mr-ballistocardiography fiber-optic sensor. Sensors. 2019;19(3).

McIntosh JR, Yao J, Hong L, Faller J, Sajda P. Ballistocardiogram artifact reduction in simultaneous EEG-fMRI using deep learning. IEEE Trans Biomed Eng. 2021;68(1):78–89.

Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, Bignami EG. Artificial ıntelligence in operating room management. J Med Syst. 2024;48(1):19.

Yao Y, Li Y, Xing F, Yang Z, Li X, Jing M, et al. Prediction of postoperative nausea and vomiting in patients undergoing sedated gastrointestinal endoscopy based on machine learning. Ann Med. 2025;57(1):2570792.

Artificial ıntelligence emerging as powerful patient safety tool in pediatric anesthesia 2026. https://www.asahq.org/about-asa/newsroom/news-releases/2025/10/artificial-intelligence-emerging-as-powerful-patient-safety-tool-in-pediatric-anesthesia

Otokiti AU, Shih H ju, Williams KS. Gender and racial bias unveiled: clinical artificial intelligence (AI) and machine learning (ML) algorithms are fanning the flames of inequity. Oxf Open Digit Health. 2025;3:oqaf027.

Zhang B, Wang P, Song Y, Su Y, Zhu Y, Wang Y, et al. Applications of artificial intelligence in pediatric general surgery: a systematic review. Transl Pediatr. 2026;15(2):56.

EU Artificial Intelligence Act. Up-to-date developments and analyses of the EU AI Act 2026. https://artificialintelligenceact.eu/

Amann J, Blasimme A, Vayena E, Frey D, Madai VI. The precise4Q consortium. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310.

Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. Npj Digit Med. 2020;3(1):119.

El Tarhouny S, Farghaly A. Deskilling dilemma: brain over automation. Front Med. 13:1765692.

Pardo E, Le Cam E, Verdonk F. Artificial intelligence and nonoperating room anesthesia. Curr Opin Anaesthesiol. 2024;37(4):413–20.

Chang B, Kaye AD, Diaz JH, Westlake B, Dutton RP, Urman RD. Interventional procedures outside of the operating room: results from the national anesthesia clinical outcomes registry. J Patient Saf. 2018;14(1):9–16.

Nagrebetsky A, Gabriel RA, Dutton RP, Urman RD. Growth of nonoperating room anesthesia care in the united states: a contemporary trends analysis. Anesth Analg. 2017;124(4):1261–7.

Statement on Nonoperating Room Anesthesia Services. 2026. Available from: https://www.asahq.org/standards-and-practice-parameters/statement-on-nonoperating-room-anesthesia-services

Türk Anesteziyoloji ve Reanimasyon Derneği (TARD). Ameliyathane Dışı Anestezi Uygulamaları Kılavuzu. TARD Akademi; 2022. https://akademi.tard.org.tr

Kovacheva V, Nagle B. Opportunities of AI-powered applications in anesthesiology to enhance patient safety. Int Anesthesiol Clin. 2024;62(2):26–33.

Giri R, Firdhos S, Vida T. Artificial ıntelligence in anesthesia: enhancing precision, safety, and global access through data-driven systems. J Clin Med. 2025;14(19):6900.

American Society of Anesthesiologists (ASA). 2025. Artificial ıntelligence emerging as powerful patient safety tool in anesthesia. https://www.asahq.org/

What Is Artificial Intelligence? IBM. 2024. https://www.ibm.com/think/topics/artificial-intelligence

Joseph A, Lakshmi R. Use of artificial intelligence for preoperative anaesthesia evaluation - a systematic review. TPM. 2025;32(S3):370–7.

Fritz BA, Cui Z, Zhang M, He Y, Chen Y, Kronzer A, et al. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth. 2019;123(5):688–95.

Shi M, Huang Z, Xiao G, Xu B, Ren Q, Zhao H. Estimating the depth of anesthesia from EEG signals based on a deep residual shrinkage Network. Sensors. 2023;23(2):1008.

Shim J, Yoon W, Lee S, Chang S, Jung S, Chung J. Machine learning methods for the prediction of ıntraoperative hypotension with biosignal waveforms. Medicina (Mex). 2024;61(11):2039.

Yves D, Agarwal K, Chan J, Promoppatum P, Pattanasiricharoen A. Evaluating deep learning-based nerve segmentation in brachial plexus ultrasound under realistic data constraints. arXiv. 2026.

Lakhani P, Flanders A, Gorniak R. Endotracheal tube position assessment on chest radiographs using deep learning. Radiol Artif Intell. 2020;3(1):e200026.

Xu NY, Litake O, Tully JL, Meineke MN, Sinha A, Meyer M, et al. A pre-trained language model approach for triaging surgical patients for preoperative anesthesia clinics. J Clin Monit Comput. 2026;40(2):517-24.

Chung P, Fong CT, Walters AM, Yetisgen M, O’Reilly-Shah VN. Prediction of American Society of Anesthesiologists Physical Status Classification from preoperative clinical text narratives using natural language processing. BMC Anesthesiol. 2023;23(1):296.

Krenzer A, Banck M, Makowski K, Hekalo A, Fitting D, Troya J, et al. A real-time polyp-detection system with clinical application in colonoscopy using deep convolutional neural networks. J Imaging. 2023;9(2):26.

Jeffries SD, Pelletier ED, Song K, Tu Z, Sinha A, Hemmerling TM. Recognition of vocal cords during videolaryngoscopy based on state-of-the-art YOLO-V8 architecture. Anesth Analg. 2025;140(5):1227–9.

Eastwood P, Gilani SZ, McArdle N, Hillman D, Walsh J, Maddison K, et al. Predicting sleep apnea from three-dimensional face photography. J Clin Sleep Med. 2020;16(4):493-502.

Cascella M. The complex task of modelling artificial intelligence workflows for forecasting postoperative risk. J Anesth Analg Crit Care. 2025;5(1):82.

Syed S, Syed M, Prior F, Zozus M, Syeda HB, Greer ML, et al. Machine learning approach to optimize sedation use in endoscopic procedures. Stud Health Technol Inform. 2021;281:183–7.

Kaushikan MP, Muthukumar R, Balaji D, Rajasekaran S, Prabakaran S, Navin RBN, et al. Clinical questionnaire-based aı for obstructive sleep apnea risk prediction: a comparative analysis of machine learning models. Indian J Otolaryngol Head Neck Surg. 2026;78:2031-38.

Hayasaka T, Kawano K, Kurihara K, Suzuki H, Nakane M, Kawamae K. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care. 2021;9(1):38

Dost B, Turan Eİ, Aydın ME, Ahıskalıoğlu A, Narayanan M, Yılmaz R, et al. Artificial ıntelligence in anaesthesiology: current applications, challenges, and future directions. Turk J Anaesthesiol Reanim. 2025;53(6):282–92.

Choi HM, Kim Y, Kim J, Park J, Lee JH, Yoon YE, et al. Artificial intelligence-enhanced ECG score for perioperative risk assessment in non-cardiac surgery. Eur Heart J Digit Health. 2026;7(2):ztag006.

News A. When ıt comes to ASA physical status, anesthesiologists and aı agree. 2026. https://www.anesthesiologynews.com/Technology/Article/12-25/When-It-Comes-to-ASA-Physical-Status-Anesthesiologists-and-AI-Agree/79062

Introna M, Karippacheril JG, Pilla S, Trimarchi D, Gemma M, Martino D, et al. Artificial intelligence and EEG during anesthesia: ideal match or fleeting bond? Artif Intell Surg. 2026;6(1):1–17.

Li T, Huang Y, Wen P, Li Y. Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features. Brain Inform. 2024;11(1):28.

Park Y, Han SH, Byun W, Kim JH, Lee HC, Kim SJ. A Real-time depth of anesthesia monitoring system based on deep neural network with large EDO tolerant EEG analog front-end. IEEE Trans Biomed Circuits Syst. 2020;14(4):825–37.

Alsayed TK, Almalki RF, Aljumah MS, Habib FM, Alhumaidan IA, Almuteri TM, et al. Hybrid electroencephalogram-genomic deep learning for personalised depth of anaesthesia monitoring: a transformer-based depth of anaesthesia ındex calculator with real-time pharmacogenomic adaptation. J Adv Trends Med Res. 2025;2(3):573–80.

Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology. 2018;129(4):663–74.

Ripollés-Melchor J, Ruiz-Escobar A, Fernández-Valdes-Bango P, Lorente JV, Jiménez-López I, Abad-Gurumeta A, et al. Hypotension prediction index: from reactive to predictive hemodynamic management, the key to maintaining hemodynamic stability. Front Anesthesiol. 2023;2:1-16.

Sarhadi K, Hamman J, Avila J, Jian Z, Fleming NW. Hypotension prediction index: comparison between invasive and non-invasive pressure inputs. BMC Anesthesiol. 2025;25(1):221.

Valbuena-Bueno MA, Ripollés-Melchor J, Ruiz-Escobar A, Fernández-Valdes-Bango P, Lorente JV, Abad-Gurumeta A, et al. Hypotension prediction index decision support system: a new model for decision support in hemodynamic management. Front Anesthesiol. 2024;3:1-8.

Sriganesh K, Francis T, Mishra RK, Prasad NN, Chakrabarti D. Hypotension prediction index for minimising intraoperative hypotension: a systematic review and meta-analysis of randomised controlled trials. Indian J Anaesth. 2024;68(11):942–50.

Khanna AK, Bergese SD, Jungquist CR, Morimatsu H, Uezono S, Lee S, et al. Prediction of opioid-ınduced respiratory depression on ınpatient wards using continuous capnography and oximetry: an ınternational prospective, observational trial. Anesth Analg. 2020;131(4):1012–24.

Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2(10):749–60.

Park K, Kim NY, Kim KJ, Oh C, Chae D, Kim SY. A Simple risk scoring system for predicting the occurrence of aspiration pneumonia after gastric endoscopic submucosal dissection. Anesth Analg. 2022;134(1):114–22.

Gungor I, Gunaydin B, Buyukgebiz Yeşil BM, Bagcaz S, Ozdemir MG, Inan G, et al. Evaluation of the effectiveness of artificial intelligence for ultrasound guided peripheral nerve and plane blocks in recognizing anatomical structures. Ann Anat Anat Anz Off Organ Anat Ges. 2023;250:152143.

Bowness JS, Macfarlane AJR, Burckett-St Laurent D, Harris C, Margetts S, Morecroft M, et al. Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia. Br J Anaesth. 2023;130(2):226–33.

Jo Y, Baek S, Baek D, Oh C, Lee D, Hong B. Artificial intelligence in ultrasound-guided regional anesthesia: bridging the gap between potential and practice: a narrative review. Anesth Pain Med. 2025;20(4):357–70.

Desebbe O, Rinehart J, Van der Linden P, Cannesson M, Delannoy B, Vigneron M, et al. Control of postoperative hypotension using a closed-loop system for norepinephrine ınfusion in patients after cardiac surgery: a randomized trial. Anesth Analg. 2022;134(5):964–73.

Rinehart J, Desebbe O, Berna A, Lam I, Coeckelenbergh S, Cannesson M, et al. Systolic arterial pressure control using an automated closed-loop system for vasopressor ınfusion during ıntermediate-to-high-risk surgery: a feasibility study. J Pers Med. 2022;12(10):1554.

Artificial ıntelligence for smart procedural sedation in the gastrointestinal endoscopy süite. 2026. https://www.fortunejournals.com/articles/artificial-intelligence-for-smart-procedural-sedation-in-the-gastrointestinal-endoscopy-suite.html

Pasin L, Nardelli P, Pintaudi M, Greco M, Zambon M, Cabrini L, et al. Closed-Loop delivery systems versus manually controlled administration of total IV anesthesia: a meta-analysis of randomized clinical trials. Anesth Analg. 2017;124(2):456–64.

Xu C, Zhu Y, Wu L, Yu H, Liu J, Zhou F, et al. Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial. BMC Anesthesiol. 2022;22(1):313.

Woodward ZG, Urman RD, Domino KB. Safety of non-operating room anesthesia: a closed claims update. Anesthesiol Clin. 2017;35(4):569–81.

Nedoma J, Fajkus M, Martinek R, Nazeran H. Vital sign monitoring and cardiac triggering at 1.5 tesla: a practical solution by an mr-ballistocardiography fiber-optic sensor. Sensors. 2019;19(3).

McIntosh JR, Yao J, Hong L, Faller J, Sajda P. Ballistocardiogram artifact reduction in simultaneous EEG-fMRI using deep learning. IEEE Trans Biomed Eng. 2021;68(1):78–89.

Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, Bignami EG. Artificial ıntelligence in operating room management. J Med Syst. 2024;48(1):19.

Yao Y, Li Y, Xing F, Yang Z, Li X, Jing M, et al. Prediction of postoperative nausea and vomiting in patients undergoing sedated gastrointestinal endoscopy based on machine learning. Ann Med. 2025;57(1):2570792.

Artificial ıntelligence emerging as powerful patient safety tool in pediatric anesthesia 2026. https://www.asahq.org/about-asa/newsroom/news-releases/2025/10/artificial-intelligence-emerging-as-powerful-patient-safety-tool-in-pediatric-anesthesia

Otokiti AU, Shih H ju, Williams KS. Gender and racial bias unveiled: clinical artificial intelligence (AI) and machine learning (ML) algorithms are fanning the flames of inequity. Oxf Open Digit Health. 2025;3:oqaf027.

Zhang B, Wang P, Song Y, Su Y, Zhu Y, Wang Y, et al. Applications of artificial intelligence in pediatric general surgery: a systematic review. Transl Pediatr. 2026;15(2):56.

EU Artificial Intelligence Act. Up-to-date developments and analyses of the EU AI Act 2026. https://artificialintelligenceact.eu/

Amann J, Blasimme A, Vayena E, Frey D, Madai VI. The precise4Q consortium. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310.

Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. Npj Digit Med. 2020;3(1):119.

El Tarhouny S, Farghaly A. Deskilling dilemma: brain over automation. Front Med. 13:1765692.