Literature DB >> 33426667

The use of artificial intelligence and robotics in regional anaesthesia.

M McKendrick1,2, S Yang3, G A McLeod4,5.   

Abstract

The current fourth industrial revolution is a distinct technological era characterised by the blurring of physics, computing and biology. The driver of change is data, powered by artificial intelligence. The UK National Health Service Topol Report embraced this digital revolution and emphasised the importance of artificial intelligence to the health service. Application of artificial intelligence within regional anaesthesia, however, remains limited. An example of the use of a convoluted neural network applied to visual detection of nerves on ultrasound images is described. New technologies that may impact on regional anaesthesia include robotics and artificial sensing. Robotics in anaesthesia falls into three categories. The first, used commonly, is pharmaceutical, typified by target-controlled anaesthesia using electroencephalography within a feedback loop. Other types include mechanical robots that provide precision and dexterity better than humans, and cognitive robots that act as decision support systems. It is likely that the latter technology will expand considerably over the next decades and provide an autopilot for anaesthesia. Technical robotics will focus on the development of accurate sensors for training that incorporate visual and motion metrics. These will be incorporated into augmented reality and visual reality environments that will provide training at home or the office on life-like simulators. Real-time feedback will be offered that stimulates and rewards performance. In discussing the scope, applications, limitations and barriers to adoption of these technologies, we aimed to stimulate discussion towards a framework for the optimal application of current and emerging technologies in regional anaesthesia.
© 2021 Association of Anaesthetists.

Entities:  

Keywords:  artificial intelligence; regional anaesthesia; robotics; technology; ultrasonography

Mesh:

Year:  2021        PMID: 33426667     DOI: 10.1111/anae.15274

Source DB:  PubMed          Journal:  Anaesthesia        ISSN: 0003-2409            Impact factor:   6.955


  5 in total

Review 1.  Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis.

Authors:  Junjie Mi; Xiaofang Han; Rong Wang; Ruijun Ma; Danyu Zhao
Journal:  Int J Clin Pract       Date:  2022-03-19       Impact factor: 3.149

2.  Modern Machine Learning Practices in Colorectal Surgery: A Scoping Review.

Authors:  Stephanie Taha-Mehlitz; Silvio Däster; Laura Bach; Vincent Ochs; Markus von Flüe; Daniel Steinemann; Anas Taha
Journal:  J Clin Med       Date:  2022-04-26       Impact factor: 4.964

Review 3.  Artificial intelligence, nano-technology and genomic medicine: The future of anaesthesia.

Authors:  Shagufta Naaz; Adil Asghar
Journal:  J Anaesthesiol Clin Pharmacol       Date:  2022-01-28

4.  Regional anaesthesia research - where to now?

Authors:  Rachel J Kearns; Jonathan Womack; Alan Jr Macfarlane
Journal:  Br J Pain       Date:  2022-04-08

5.  A Study on the Role of Intelligent Medical Simulation Systems in Teaching First Aid Competence in Anesthesiology.

Authors:  Wei He; Jiayu Lu; Wei Zheng; Xingyu Zhang; Zhaoxiang Yu; Lin Shen; Duo Zhang
Journal:  J Healthc Eng       Date:  2022-04-21       Impact factor: 3.822

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.