Keywords
Medical practice
Anesthesiology
How to Cite
Copyright © | All rights reserved.
The journal holds the exclusive copyright for the publication of this article under the terms of Brazilian law 9610/98.
Partial reproduction
The use of parts of the texts, figures and questionnaire of the article is free, being mandatory the citation of the authors and journal.
Total reproduction
It is expressly prohibited and must be authorized by the journal.
Abstract
Objective: To analyze the applicability of Artificial Intelligence (AI) in anesthesiology, highlighting its benefits and challenges in medical practice. Methods: An integrative review conducted in the PubMed Central (PMC) database using the terms "artificial intelligence" and "anesthesiology" for the period from 2019 to 2024. A total of 1,150 articles were identified, and 15 were selected for detailed analysis. Results: AI demonstrated several important applications in anesthesiology, such as improving sedative techniques, enhancing the assessment of difficult airways before intubation, and predicting complications in invasive procedures. Furthermore, AI has the potential to facilitate access to medical information, promoting better patient understanding of their health. Conclusion: While AI offers numerous advantages in anesthesiology, more studies are needed to explore its ethical implications and technological challenges. The lack of proper education on AI may hinder its adoption in anesthetic practice. However, AI has the potential to be a valuable complementary tool, assisting anesthesiologists and optimizing therapeutic success, without replacing the human role in decision-making.
References
CAI N, et al. Examining the impact perceptual learning artificial-intelligence-based on the incidence of paresthesia when performing the ultrasound-guided popliteal sciatic block: simulation-based randomized study. BMC Anesthesiology. 2022; 22(1): 392.
CHEN J, et al. Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network. CellPress Heliyon. 2023; 9(1): 12481.
DE ROSA SB, et al. The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management. Anesthesia & Analgesia. 2022; 10: 1213.
FROESE L, et al. Computer Vision for Continuous Bedside Pharmacological Data Extraction: A Novel Application of Artificial Intelligence for Clinical Data Recording and Biomedical Research. Frontiers in Big Data. 2021; 4: 1-10.
GARG S e MUKUL CK. Role of artificial intelligence in perioperative monitoring in anaesthesia. Indian Journal of Anaesthesia. 2024; 68(1): 87-92.
GONDODE P, et al. Comparative Analysis of Accuracy, Readability, Sentiment, and Actionability: Artificial Intelligence Chatbots (ChatGPT and Google Gemini) versus Traditional Patient Information Leaflets for Local Anesthesia in Eye Surgery. British and Irish Orthoptic Journal. 2024; 20(1):1 83–192.
GONZALEZ-CAVA JM, et al. Machine learning based method for the evaluation of the Analgesia Nociception Index in the assessment of general anesthesia. Computers in Biology and Medicine. 2020; 118: 103645.
GUNGOR I, et al. A real-time anatomy identification via tool based on artificial intelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study. Journal of Anesthesia. 2021; 35: 591-594.
HAIASAKA T, et al. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. Journal of Intensive Care. 2021; 9: 38.
HENCKERT D, et al. Attitudes of Anesthesiologists toward Artificial Intelligence in Anesthesia: A Multicenter, Mixed Qualitative–Quantitative Study. Journal of Clinical Medicine. 2023; 12: 2096.
KANG AR, et al. Development of a prediction model for hypotension after induction of anesthesia using machine learning. PLOS ONE. 2020; 15(4): 231172.
KANG H, et al. Machine-Learning Model for the Prediction of Hypoxaemia during Endoscopic Retrograde Cholangiopancreatography under Monitored Anaesthesia Care. Yonsei Medical Journal. 2023; 64(1): 25.
LOPES S, et al. Artificial intelligence and its clinical application in Anesthesiology: a systematic review. Journal of Clinical Monitoring and Computing. 2024; 38: 247–259.
MACIAG TT, et al. Machine learning in anesthesiology: Detecting adverse events in clinical practice. Health Informatics Journal. 2022; 28(3): 14604582221112855.
MIKA S, et al. Artificial Intelligence-Supported Ultrasonography in Anesthesiology: Evaluation of a Patient in the Operating Theatre. Journal of Personalized Medicine. 2024; 14(3): 310.
NAIK NB, et al. Scope of artificial intelligence in airway management. Indian Journal of Anaesthesia. 2024; 68(1): 105-110.
RAMASWAMY SM, et al. Frontal electroencephalogram based drug, sex, and age independent sedation level prediction using non-linear machine learning algorithms. Journal of Clinical Monitoring and Computing. 2020; 34(4): 1-10.
SONG Z, et al. Knowledge, Attitudes and Practices Among Anesthesia and Thoracic Surgery Medical Staff Toward Ai-PCA. Journal of Multidisciplinary Healthcare. 2024; 17: 3295-3304.
WANG G, et al. A fully-automatic semi-supervised deep learning model for difficult airway assessment. Heliyon. 2023; 9: 15629.
XU C, 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 Anesthesiology. 2022; 22: 313.