Aplicações da inteligência artificial na otimização da anestesia veterinária e humana: avanços, segurança e desafios éticos

Autores

  • Nayara Ayres de Lacerda Gomes
  • Anderson Farias
  • Lucas Medeiros Mota
  • Aline Moreira Hupalo
  • Clara Teixeira Barros
  • Daniel Medeiros Feitosa
  • Gabrielle Fernandes Nogueira de Carvalho
  • Mariana Cesar Sousa
  • Loïc Alexandre Rangel Lamarche

DOI:

https://doi.org/10.34188/bjaerv8n4-043

Palavras-chave:

inteligência artificial, anestesia, hemodinâmica, ética, infusão alvo-controlada

Resumo

A inteligência artificial (IA) tem trazido uma nova abordagem a anestesiologia veterinária e humana, introduzindo ferramentas capazes de otimizar o controle farmacológico, o monitoramento fisiológico e a segurança anestésica. Este artigo tem como objetivo revisar e discutir as principais aplicações da IA na anestesia, destacando avanços tecnológicos, implicações éticas, riscos hemodinâmicos e o papel das bombas alvo controladas por modelo farmacocinético (Target-Controlled Infusion, TCI). A metodologia baseou-se em uma revisão bibliográfica integrativa de artigos científicos publicados entre 2018 e 2024, obtidos em bases indexadas nacionais e internacionais. As evidências indicam que o uso de algoritmos de aprendizado de máquina e sistemas de suporte à decisão clínica permite prever instabilidades hemodinâmicas, ajustar doses de anestésicos em tempo real e reduzir complicações intraoperatórias e otimizar a recuperação anestésica. Na medicina veterinária, embora o uso ainda seja pouco difundido, os resultados mostram potencial para padronizar protocolos e minimizar a variabilidade entre profissionais. Todavia, os desafios éticos e regulatórios, como a transparência algorítmica e a preservação da autonomia do anestesiologista, permanecem em debate. Conclui-se que a IA representa uma ferramenta promissora na anestesia, mas seu uso deve ser pautado por evidências científicas, segurança, conhecimento técnico do profissional que usa e rigor ético.

   

Biografia do Autor

Nayara Ayres de Lacerda Gomes

Anderson Farias

Lucas Medeiros Mota

Aline Moreira Hupalo

Clara Teixeira Barros

Daniel Medeiros Feitosa

Gabrielle Fernandes Nogueira de Carvalho

Mariana Cesar Sousa

Loïc Alexandre Rangel Lamarche

Referências

ABSALOM, A.; GLEN, J.; ZWART, G.; SCHNIDER, T.; STRUYS, M. Target-Controlled Infusion: A Mature Technology. Anesthesia & Analgesia, v.122, p.70–78, 2016. https://doi.org/10.1213/ANE.0000000000001009

AKINSULIE, A. O.; et al. Artificial Intelligence Applications in Veterinary Anesthesia and Surgery: A Review.Frontiers in Veterinary Science, v.11, 1379021, 2024.

AKINSULIE, O.; IDRIS, I.; ALIYU, V.; et al. The potential application of artificial intelligence in veterinary clinical practice and biomedical research. Frontiers in Veterinary Science, v.11, 2024. https://doi.org/10.3389/fvets.2024.1347550

APPLEBY, R.; BASRAN, P. Artificial intelligence in veterinary medicine. Journal of the American Veterinary Medical Association, p.1–6, 2022. https://doi.org/10.2460/javma.22.03.0093

BELLINI, V.; CARNÀ, E.; RUSSO, M.; et al. Artificial intelligence and anesthesia: a narrative review. Annals of Translational Medicine, v.10, 2021. https://doi.org/10.21037/atm-21-7031

BOGOŃ, A.; GÓRSKA, M.; OSTOJSKA, M.; et al. Artificial intelligence in anesthesiology – a review. Journal of Pre-Clinical and Clinical Research, 2024. https://doi.org/10.26444/jpccr/191550

BOWNESS, J.; METCALFE, D.; EL-BOGHDADLY, K.; et al. Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines. British Journal of Anaesthesia, v.132, p.1049–1062, 2024. https://doi.org/10.1016/j.bja.2024.01.036

CARNEIRO, R.; PEREIRA, L. Depth of Anesthesia Monitoring and Artificial Intelligence. Current Anesthesiology Reports, 2025. https://doi.org/10.1007/s40140-024-00655-8

CASCELLA, M.; TRACEY, M.; PETRUCCI, E.; BIGNAMI, E. Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics and Clinical Applications. Surgeries, 2023. https://doi.org/10.3390/surgeries4020027

CUNIBERTI, B.; HUUSKONEN, V.; HUGHES, J. Comparison between continuous rate infusion and target-controlled infusion of propofol in dogs: a randomized clinical trial. Veterinary Anaesthesia and Analgesia, 2021. https://doi.org/10.1016/j.vaa.2021.08.048

CRUZ, H. N. da; PRATES, F. B. da C.; CARVALHO, E. dos S. R.; SILVA, E. de A. V.; ARAÚJO, M. C. A. de; NASCIMENTO, M. M. R. “IAgora: como o uso de tecnologias influencia a formação do profissional”, Brazilian Journal of Animal and Environmental Research, 8(4), p. e82719. 2025. https://doi:10.34188/bjaerv8n4-003

DACCACHE, N.; ZAKO, J.; MORISSON, L.; LAFERRIÈRE-LANGLOIS, P. As aplicações do ChatGPT e outros grandes modelos de linguagem em anestesiologia e cuidados intensivos: uma revisão sistemática. Canadian Journal of Anaesthesia, v.72, n.6, p.904–922, 2025. https://doi.org/10.1007/s12630-025-02973-9

HEMMERLING, T. Robots Will Perform Anesthesia in the Near Future. Anesthesiology, 2019. https://doi.org/10.1097/ALN.0000000000003088

HEMMERLING, T.; JEFFRIES, S. Robotic Anesthesia: A Vision for 2050. Anesthesia & Analgesia, v.138, n.2, p.239–251, 2024. https://doi.org/10.1213/ANE.0000000000006835

HENCKERT, D.; MALORGIO, A.; SCHWEIGER, G.; et al. Attitudes of Anesthesiologists toward Artificial Intelligence in Anesthesia: A Multicenter, Mixed Qualitative–Quantitative Study. Journal of Clinical Medicine, v.12, 2023. https://doi.org/10.3390/jcm12062096

HUUSKONEN, V.; HUGHES, J. In vitro delivery accuracy of Computer Controlled Infusion Pump (CCIP) software linked to an Alaris® GH syringe pump for propofol target-controlled infusion in dogs. Medycyna Weterynaryjna, v.74, 2018. https://doi.org/10.21521/MW.5988

KAPOOR, S.; ARVIND, A.; KLOUB, G.; et al. Evolution of Artificial Intelligence in Anesthesia and Its Path Forward. International Journal of Advanced Research, 2024. https://doi.org/10.21474/ijar01/19608

LOFTUS, T.; TIGHE, P.; FILIBERTO, A.; et al. Artificial Intelligence and Surgical Decision-Making. JAMA Surgery, 2019. https://doi.org/10.1001/jamasurg.2019.4917

LOPES, R.; CARNEIRO, F.; PEREIRA, M. Machine Learning na Anestesiologia Veterinária: Tendências e Desafios Éticos. Revista Brasileira de Anestesiologia Veterinária, v.3, n.1, p.15–29, 2025.

LOPES, S.; ROCHA, G.; GUIMARÃES-PEREIRA, L. Artificial intelligence and its clinical application in Anesthesiology: a systematic review. Journal of Clinical Monitoring and Computing, v.38, p.247–259, 2023. https://doi.org/10.1007/s10877-023-01088-0

LONSDALE, H.; BURNS, M.; EPSTEIN, R.; et al. Strengthening Discovery and Application of Artificial Intelligence in Anesthesiology: A Report from the Anesthesia Research Council. Anesthesia & Analgesia, v.140, p.920–930, 2025. https://doi.org/10.1213/ANE.0000000000007474

MATHIS, M.; KHETERPAL, S.; NAJARIAN, K. Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know. Anesthesiology, v.129, p.619–622, 2018. https://doi.org/10.1097/ALN.0000000000002384

MATHIS, M.; STEFFNER, K.; SUBRAMANIAN, H.; et al. Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology. Journal of Cardiothoracic and Vascular Anesthesia, 2024. https://doi.org/10.1053/j.jvca.2024.02.004

MCKENDRICK, M.; YANG, S.; MCLEOD, G. The use of artificial intelligence and robotics in regional anaesthesia.Anaesthesia, v.76, 2021. https://doi.org/10.1111/anae.15274

MENG, E.; HOANG, T. MEMS-enabled implantable drug infusion pumps for laboratory animal research, preclinical, and clinical applications. Advanced Drug Delivery Reviews, v.64, n.14, p.1628–1638, 2012. https://doi.org/10.1016/j.addr.2012.08.006

MIRZA, M.; MEHTAB, M.; ISLAM, B.; SHAHBAZ, S. Artificial Intelligence for Perioperative Medicine in Anesthesia: Present State and Future Perspectives of AI in Anesthesiology. The Healer Journal of Physiotherapy and Rehabilitation Sciences, v.5, n.1, 2025. https://doi.org/10.55735/hjprs.v5i1.330

NADA, I.; NGUYEN, T.; GUO, N.; et al. The evaluation of the performance of ChatGPT in the management of labor analgesia. Journal of Clinical Anesthesia, v.98, 2024. https://doi.org/10.1016/j.jclinane.2024.111582

NIEUWENHUYZEN, M.; ENGBERS, F.; VUYK, J.; BURM, A. Target-Controlled Infusion Systems. Clinical Pharmacokinetics, v.38, p.181–190, 2000. https://doi.org/10.2165/00003088-200038020-00003

PARDO, E.; CAM, L.; VERDONK, F. Artificial intelligence and nonoperating room anesthesia. Current Opinion in Anaesthesiology, v.37, p.413–420, 2024. https://doi.org/10.1097/ACO.0000000000001388

REN, W.; CHEN, J.; LIU, J.; et al. Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network. Heliyon, v.9, 2022. https://doi.org/10.1016/j.heliyon.2022.e12481

SCHNIDER, T.; MINTO, C.; STRUYS, M.; ABSALOM, A. The Safety of Target-Controlled Infusions. Anesthesia & Analgesia, v.122, p.79–85, 2016. https://doi.org/10.1213/ANE.0000000000001005

SINGAM, A. Revolutionizing Patient Care: A Comprehensive Review of Artificial Intelligence Applications in Anesthesia. Cureus, v.15, 2023. https://doi.org/10.7759/cureus.49887

SINGHAL, M.; GUPTA, L.; HIRANI, K. A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia. Cureus, v.15, 2023. https://doi.org/10.7759/cureus.45038

SINGH, M.; NATH, G. Artificial intelligence and anesthesia: A narrative review. Saudi Journal of Anaesthesia, v.16, p.86–93, 2022. https://doi.org/10.4103/sja.sja_669_21

SINGHAL, A.; et al. Machine Learning and Artificial Intelligence in Anesthesia: The Future Is Now. Journal of Clinical Anesthesia, v.86, 111081, 2023.

SONG, B.; ZHOU, M.; ZHU, J. Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security. Medical Science Monitor, v.29, 2023. https://doi.org/10.12659/MSM.938835

STRUYS, M.; DE SMET, T.; GLEN, J.; et al. The History of Target-Controlled Infusion. Anesthesia & Analgesia, v.122, p.56–69, 2016. https://doi.org/10.1213/ANE.0000000000001008

VIDERMAN, D.; DOSSOV, M.; SEITENOV, S.; LEE, M. Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review. Frontiers in Medicine, v.9, 2022. https://doi.org/10.3389/fmed.2022.994805

YUAN, L.; LI, G.; TENG, Z.; TENG, J. Application Analysis of Artificial Intelligence Target Controlled Infusion in Clinical Anesthesia Operation. International Conference on Artificial Intelligence and Advanced Manufacture, 2021. https://doi.org/10.1145/3495018.3495389

ZENG, S.; QING, Q.; XU, W.; et al. Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors. Frontiers in Medicine, v.11, 2024. https://doi.org/10.3389/fmed.2024.1365524

ZHANG, J.; ZHANG, Z. Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making, v.23, 2023. https://doi.org/10.1186/s12911-023-02103-9

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Publicado

2025-10-24

Como Citar

Gomes , N. A. de L., Farias, A., Mota, L. M., Hupalo , A. M., Barros, C. T., Feitosa, D. M., Carvalho, G. F. N. de, Sousa, M. C., & Lamarche, L. A. R. (2025). Aplicações da inteligência artificial na otimização da anestesia veterinária e humana: avanços, segurança e desafios éticos. Brazilian Journal of Animal and Environmental Research, 8(4), e83073. https://doi.org/10.34188/bjaerv8n4-043

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