REVIEW PAPER
Artificial intelligence in anesthesiology – a review
1
Department of Anesthesiology and Intensive Care, 1st Military Clinical Hospital with Polyclinic, Lublin, Poland
2
Emergency Department, 5th Military Clinical Hospital with Polyclinic, Kraków, Poland
3
Emergency Department, Wolski Hospital, Warsaw, Poland
4
Faculty of Medical Sciences and Health Sciences, Casimir Pulaski University, Radom, Poland
Corresponding author
Aleksandra Bogoń
Department of Anesthesiology and Intensive Care, 1st Military Clinical Hospital with Polyclinic in Lublin
Department of Anesthesiology and Intensive Care, 1st Military Clinical Hospital with Polyclinic in Lublin
J Pre Clin Clin Res. 2024;18(3):265-269
KEYWORDS
TOPICS
ABSTRACT
Introduction and objective:
Introduction. Artificial Intelligence (AI) is a field of computer science where hardware and software systems enable machines to think and act like humans. AI utilizes different algorithms and computational resources to perform intelligent tasks autonomously. AI techniques applied in clinical decision support have proven effective across various medical disciplines, including clinical anaesthesia.
Objective:
The aim of this review is to explore the areas of anaesthesiology where artificial intelligence is utilized, the benefits of this implementation, and to define ethical dilemmas connected with using AI technology.
Review methods:
PubMed and Google Scholar databases were searched using key words. Original articles in English, published between 2018–2024 were included.
Brief description of the state of knowledge:
AI is a rapidly developing field, notable particularly for its increasing application across various domains, including anaesthesiology. The amount of research on AI in anesthesia is growing
Summary:
AI has shown considerable promise in various aspects of anaesthesiology, from pre-operative to post-operative care. AI-driven systems predict patient risk, manage drug dosages, administer drugs, and monitor vital signs more effectively than traditional methods. Its applications in anaesthesia can enhance patient outcomes through more personalized and precise interventions, optimize resource allocation, and improve overall efficiency in clinical practice. It also facilitates real-time decision-making and pro-active management of potential complications. However, AI cannot entirely replace the nuanced understanding and empathetic care provided by human professionals. As AI technology advances, legal and ethical standards must also evolve.
Introduction. Artificial Intelligence (AI) is a field of computer science where hardware and software systems enable machines to think and act like humans. AI utilizes different algorithms and computational resources to perform intelligent tasks autonomously. AI techniques applied in clinical decision support have proven effective across various medical disciplines, including clinical anaesthesia.
Objective:
The aim of this review is to explore the areas of anaesthesiology where artificial intelligence is utilized, the benefits of this implementation, and to define ethical dilemmas connected with using AI technology.
Review methods:
PubMed and Google Scholar databases were searched using key words. Original articles in English, published between 2018–2024 were included.
Brief description of the state of knowledge:
AI is a rapidly developing field, notable particularly for its increasing application across various domains, including anaesthesiology. The amount of research on AI in anesthesia is growing
Summary:
AI has shown considerable promise in various aspects of anaesthesiology, from pre-operative to post-operative care. AI-driven systems predict patient risk, manage drug dosages, administer drugs, and monitor vital signs more effectively than traditional methods. Its applications in anaesthesia can enhance patient outcomes through more personalized and precise interventions, optimize resource allocation, and improve overall efficiency in clinical practice. It also facilitates real-time decision-making and pro-active management of potential complications. However, AI cannot entirely replace the nuanced understanding and empathetic care provided by human professionals. As AI technology advances, legal and ethical standards must also evolve.
Bogoń A, Górska M, Ostojska M, Kałuża I, Dziuba G, Dobosz M. Artificial intelligence in anaesthesiology – a review. J Pre-Clin Clin Res. 2024;
18(3): 265–269. doi: 10.26444/jpccr/191550
REFERENCES (45)
1.
Cascella M, Perri F, Ottaiano A, et al. Trends in Research on Artificial Intelligence in Anaesthesia: A VOSviewer – Based Bibliometric Analysis. Inteligencia Artificial. 2022;25(70):126–137. https://doi.org/10.4114/intart....
2.
Lopes S, Rocha G, Guimarães-Pereira L. Artificial intelligence and its clinical application in Anesthesiology: a systematic review. J Clin Monit Comput. 2024;38(2):247–259. https://doi.org/10.1007/s10877....
3.
Cascella M, Tracey MC, Petrucci E, et al. Exploring Artificial Intelligence in Anaesthesia: A Primer on Ethics, and Clinical Applications. Surgeries. 2023;4(2):264–274. https://doi.org/10.3390/surger....
4.
Singam A. Revolutionizing Patient Care: A Comprehensive Review of Artificial Intelligence Applications in Anaesthesia. Cureus. 2023;15(12):e49887. https://doi.org/10.7759/cureus....
5.
Kambale M, Jadhav S. Applications of artificial intelligence in anaesthesia: A systematic review. Saudi J Anaesth. 2024;18(2):249–256. https://doi.org/10.4103/sja.sj....
6.
Kara Görmüş S. Integrative Artificial Intelligence in Regional Anaesthesia: Enhancing Precision, Efficiency, Outcomes and Limitations. JOINIHP. 2024;5(1):52–66. https://doi.org/10.58770/joini....
7.
Song B, Zhou M, Zhu J. Necessity and Importance of Developing AI in Anaesthesia from the Perspective of Clinical Safety and Information Security. Med Sci Monit. 2023;29:e938835. https://doi.org/10.12659/MSM.9....
8.
Langeron O, Castoldi N, Rognon N, et al. How anesthesiology can deal with innovation and new technologies? Minerva Anestesiol. 2024;90(1–2):68–76. https://doi.org/10.23736/S0375....
9.
Tavolara TE, Gurcan MN, Segal S, et al. Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models. Comput Biol Med. 2021;136:104737. https://doi.org/10.1016/j.comp....
10.
Kim JH, Kim H, Jang JS, et al. Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height. BMC Anesthesiol. 2021;21(1):125. https://doi.org/10.1186/s12871....
11.
Hayasaka T, Kawano K, Kurihara K, et al. 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. https://doi.org/10.1186/s40560....
12.
Singh M, Nath G. Artificial intelligence and anaesthesia: A narrative review. Saudi J Anaesth. 2022;16(1):86–93. https://doi.org/10.4103/sja.sj....
13.
Bellini V, Valente M, Gaddi AV, et al. Artificial intelligence and telemedicine in anaesthesia: potential and problems. Minerva Anestesiol. 2022;88(9):729–734. https://doi.org/10.23736/S0375....
14.
Cascella M, Montomoli J, Bellini V. Writing the paper “Unveiling artificial intelligence: an insight into ethics and applications in anaesthesia” implementing the large language model ChatGPT: a qualitative study. J Med Artif Intell. 2023;6:9. https://doi.org/10.21037/jmai-....
15.
Hofer IS, Lee C, Gabel E, et al. Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set. NPJ Digit Med. 2020;3:58. https://doi.org/10.1038/s41746....
16.
Zhang L, Fabbri D, Lasko TA, et al. A System for Automated Determination of Perioperative Patient Acuity. J Med Syst. 2018;42(7):123. https://doi.org/10.1007/s10916....
17.
Kendale S, Kulkarni P, Rosenberg AD, et al. Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension. Anesthesiology. 2018;129(4):675–688. https://doi.org/10.1097/ALN.00....
18.
Hatib F, Jian Z, Buddi S, et al. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology. 2018;129(4):663–674. https://doi.org/10.1097/ALN.00....
19.
Bihorac A, Ozrazgat-Baslanti T, Ebadi A, et al. MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery. Ann Surg. 2019;269(4):652–662. https://doi.org/10.1097/SLA.00....
20.
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(3):e212240. https://doi.org/10.1001/jamane....
21.
Lee CK, Hofer I, Gabel E, et al. Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality. Anesthesiology. 2018;129(4):649–662. https://doi.org/10.1097/ALN.00....
22.
Fritz BA, Cui Z, Zhang M, et al. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth. 2019;123(5):688–695. https://doi.org/10.1016/j.bja.....
23.
Hill BL, Brown R, Gabel E, et al. An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data. Br J Anaesth. 2019;123(6):877–886. https://doi.org/10.1016/j.bja.....
24.
Chiew CJ, Liu N, Wong TH, et al. Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission. Ann Surg. 2020;272(6):1133–1139. https://doi.org/10.1097/SLA.00....
25.
Jauk S, Kramer D, Stark G, et al. Development of a Machine Learning Model Predicting an ICU Admission for Patients with Elective Surgery and Its Prospective Validation in Clinical Practice. Stud Health Technol Inform. 2019;264:173–177. https://doi.org/10.3233/SHTI19....
26.
Singhal M, Gupta L, Hirani K. A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia. Cureus. 2023;15(9):e45038. https://doi.org/10.7759/cureus....
27.
Vidhya KS, Sultana A, Naveen Kumar M, et al. Artificial Intelligence’s Impact on Drug Discovery and Development From Bench to Bedside. Cureus. 2023;15(10):e47486. https://doi.org/10.7759/cureus....
28.
Syrowatka A, Song W, Amato MG, et al. Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. Lancet Digit Health. 2022;4(2):e137-e148. https://doi.org/10.1016/S2589-....
29.
Lee HC, Ryu HG, Chung EJ, et al. Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach. Anesthesiology. 2018;128(3):492–501. https://doi.org/10.1097/ALN.00....
30.
Miyaguchi N, Takeuchi K, Kashima H, et al. Predicting anesthetic infusion events using machine learning. Sci Rep. 2021;11(1):23648. https://doi.org/10.1038/s41598....
31.
Syed S, Syed M, Prior F, et al. Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures. Stud Health Technol Inform. 2021;281:183–187. https://doi.org/10.3233/SHTI21....
32.
Wei CN, Wang LY, Chang XY, et al. A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section. BMC Anesthesiol. 2021;21(1):116. https://doi.org/10.1186/s12871....
33.
Mendez JA, Leon A, Marrero A, et al. Improving the anesthetic process by a fuzzy rule based medical decision system. Artif Intell Med. 2018;84:159–170. https://doi.org/10.1016/j.artm....
34.
Xu C, Zhu Y, Wu L, et al. Evaluating the effect of an artificial intelligence system on the anaesthesia quality control during gastrointestinal endoscopy with sedation: a randomized controlled trial. BMC Anesthesiol. 2022;22(1):313. https://doi.org/10.1186/s12871....
35.
Bellini V, Rafano Carnà E, Russo M, et al. Artificial intelligence and anaesthesia: a narrative review. Ann Transl Med. 2022;10(9):528. https://doi.org/10.21037/atm-2....
36.
Gu Y, Liang Z, Hagihira S. Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anaesthesia. Sensors (Basel). 2019;19(11):2499. https://doi.org/10.3390/s19112....
37.
Tacke M, Kochs EF, Mueller M, et al. Machine learning for a combined electroencephalographic anaesthesia index to detect awareness under anaesthesia. PLoS One. 2020;15(8):e0238249. https://doi.org/10.1371/journa....
38.
Shalbaf A, Saffar M, Sleigh JW, et al. Monitoring the Depth of Anaesthesia Using a New Adaptive Neurofuzzy System. IEEE J Biomed Health Inform. 2018;22(3):671–677. https://doi.org/10.1109/JBHI.2....
39.
Park Y, Han SH, Byun W, et al. A Real-Time Depth of Anaesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End. IEEE Trans Biomed Circuits Syst. 2020;14(4):825–837. https://doi.org/10.1109/TBCAS.....
40.
Afshar S, Boostani R, Sanei S. A Combinatorial Deep Learning Structure for Precise Depth of Anaesthesia Estimation From EEG Signals. IEEE J Biomed Health Inform. 2021;25(9):3408–3415. https://doi.org/10.1109/JBHI.2....
41.
Huo J, Yu Y, Lin W, et al. Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis. J Med Internet Res. 2024;26:e51250. https://doi.org/10.2196/51250.
42.
Cascella M, Schiavo D, Cuomo A, et al. Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives. Pain Res Manag. 2023;2023:6018736. https://doi.org/10.1155/2023/6....
43.
Bowness JS, Metcalfe D, El-Boghdadly K, et al. Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines. Br J Anaesth. 2024;132(5):1049–1062. https://doi.org/10.1016/j.bja.....
44.
Hashimoto DA, Witkowski E, Gao L, et al. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020;132(2):379–394. https://doi.org/10.1097/ALN.00....
45.
Naaz S, Asghar A. Artificial intelligence, nano-technology and genomic medicine: The future of anaesthesia. J Anaesthesiol Clin Pharmacol. 2022;38(1):11–17. https://doi.org/10.4103/joacp.....
Share
RELATED ARTICLE
| eISSN: | 1898-7516 |
| ISSN: | 1898-2395 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
