Palavras-chave
Prática médica
Anestesiologia
Como Citar
Copyright © | Todos os direitos reservados.
A revista detém os direitos autorais exclusivos de publicação deste artigo nos termos da lei 9610/98.
Reprodução parcial
É livre o uso de partes do texto, figuras e questionário do artigo, sendo obrigatória a citação dos autores e revista.
Reprodução total
É expressamente proibida, devendo ser autorizada pela revista.
Resumo
Objetivo: Analisar a aplicabilidade da Inteligência Artificial (IA) na anestesiologia, destacando seus benefícios e desafios na prática médica. Métodos: Revisão integrativa realizada na base de dados PubMed Central (PMC), utilizando os termos "artificial intelligence" e "anesthesiology", abrangendo o período de 2019 a 2024. Foram identificados 1.150 artigos, dos quais 15 foram selecionados para análise detalhada. Resultados: A IA demonstrou várias aplicações importantes na anestesiologia, como o aprimoramento das técnicas sedativas, a melhoria na avaliação de via aérea difícil antes da intubação e a previsão de complicações em procedimentos invasivos. Além disso, a IA tem o potencial de facilitar o acesso a informações médicas, promovendo maior compreensão do paciente sobre sua saúde. Considerações finais: Embora a IA ofereça inúmeras vantagens na anestesiologia, são necessários mais estudos para explorar suas implicações éticas e desafios tecnológicos. A falta de educação adequada sobre IA pode dificultar sua adoção na prática anestesiológica. No entanto, a IA tem potencial para ser uma ferramenta complementar valiosa, auxiliando o anestesiologista e otimizando o sucesso terapêutico, sem substituir o papel humano no processo decisório.
Referências
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.