Reviews Open access

The integration of artificial intelligence in regional anesthesia: perspectives, challenges, and opportunities

Authors

Published
9 July 2025
Section
Reviews
DOI
10.4081/aapm.2025.51

Abstract

Artificial intelligence (AI) presents new opportunities in medicine for enhancing clinical outcomes, professional training, and healthcare organizations. We emphasize the importance of thoroughly understanding new technologies and defining their potential and limitations, promoting the construction of multidisciplinary collaborations to provide modern, comprehensive anesthesiological training.

Keywords

References

  1. 1. Görmüş SK. Integrative Artificial Intelligence in Regional Anesthesia: Enhancing Precision, Efficiency, Outcomes and Limitations. J Int Healthc Pract 2024;5:52-66. DOI: https://doi.org/10.58770/joinihp.1435169
  2. 2. Karmakar A, Khan MJ, Abdul-Rahman MEF, Shahid U. The Advances and Utility of Artificial Intelligence and Robotics in Regional Anesthesia: An Overview of Recent Developments. Cureus 2024;16:e44306. DOI: https://doi.org/10.7759/cureus.44306
  3. 3. Balavenkatasubramanian J, Kumar S, Sanjayan RD. Artificial intelligence in regional anaesthesia. Indian J Anaesth 2024;68:100-4. DOI: https://doi.org/10.4103/ija.ija_1274_23
  4. 4. Bowness J, Varsou O, Turbitt L, Burkett-St Laurent D. Identifying anatomical structures on ultrasound: Assistive artificial intelligence in ultrasound-guided regional anesthesia. Clin Anat 2021;34:802-9. DOI: https://doi.org/10.1002/ca.23742
  5. 5. Huang C, Zhou Y, Tan W, et al. Applying deep learning in recognizing the femoral nerve block region on ultrasound images. Ann Transl Med 2019;7:453. DOI: https://doi.org/10.21037/atm.2019.08.61
  6. 6. Cai N, Wang G, Xu L, et al. Examining the impact of perceptual learning artificial-intelligence-based on the incidence of paresthesia when performing the ultrasound-guided popliteal sciatic block: simulation-based randomized study. BMC Anesthesiol 2022;22:343. DOI: https://doi.org/10.1186/s12871-022-01937-6
  7. 7. Bowness JS, Macfarlane AJR, Burckett-St Laurent D, et al. Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia. Br J Anaesth 2022;130:226-33. DOI: https://doi.org/10.1016/j.bja.2022.07.049
  8. 8. Shokoohi H, LeSaux MA, Roohani YH, et al. Enhanced point-of-care ultrasound applications by integrating automated feature-learning systems using deep learning. J Ultrasound Med 2019;38:1887-97. DOI: https://doi.org/10.1002/jum.14860
  9. 9. Blaivas M, Arntfield R, White M. Creation and testing of a deep learning algorithm to automatically identify and label vessels, nerves, tendons, and bones on cross-sectional point-of-care ultrasound scans for peripheral intravenous catheter placement by novices. J Ultrasound Med 2020;39:1721-7. DOI: https://doi.org/10.1002/jum.15270
  10. 10. Delvaux B. cNerve, AI to Assist in Ultrasound-Guided Nerve Blocks. Available from: https://www.gehealthcare.com/-/jssmedia/gehc/us/files/products/ultrasound/venue-family/whitepaper-cnerve-pocus-venue-family-jb20312xx.pdf [accessed on 1 January 2024].
  11. 11. Mika S, Gola W, Gil-Mika M, et al. Artificial intelligence-supported ultrasonography in anesthesiology: evaluation of a patient in the operating theatre. J Pers Med 2024;14:310. DOI: https://doi.org/10.3390/jpm14030310
  12. 12. Choudhary N, Gupta A, Gupta N. Artificial intelligence and robotics in regional anesthesia. World J Methodol 2024;14:95762. DOI: https://doi.org/10.5662/wjm.v14.i4.95762

Metrics

Citations and online attention from Dimensions, Altmetric and PlumX.

PlumX Metrics

License

Copyright (c) 2025 the Author(s)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.