Back
 JBM  Vol.7 No.11 , November 2019
Future of Artificial Intelligence in Anesthetics and Pain Management
Abstract:
The potential of the second wave of Artificial Intelligence (AI) to change our lives beyond recognition is both exciting and challenging. AI has been around for over three decades, and this new approach of artificial intelligence, due to enhancements in technology, both software, and hardware, has resulted in the fact that human decision-making is considered inferior and erratic in many fields: none more so than medicine. Machine learning algorithms with access to large data sets can be trained to outperform clinicians in many respects. AI’s effectiveness in accurate diagnosis of various medical conditions and medical image interpretation is well documented. Modern AI technology has the potential to transform medicine to a level never seen before in terms of efficiency and accuracy; but is also potentially highly disruptive, creating insecurity and allowing the transfer of expert domain knowledge to machines. Anesthetics is a complex medical discipline and assuming AI can easily replace experienced and knowledgeable medical practitioners is a very unrealistic expectation. AI can be used in anesthetics to develop, in some respects, more advanced clinical decision support tools based on machine learning. This paper focuses on the complexity of both AI developments, deep learning, neural networks, etc. and opportunities of AI in anesthetics for the future. It will review current advances in AI tools and hardware technologies as well as outlining how these can be used in the field of anesthetics.
Cite this paper: McGrath, H. , Flanagan, C. , Zeng, L. and Lei, Y. (2019) Future of Artificial Intelligence in Anesthetics and Pain Management. Journal of Biosciences and Medicines, 7, 111-118. doi: 10.4236/jbm.2019.711010.
References

[1]   Neill, D.B. (2013) Using Artificial Intelligence to Improve Hospital Inpatient Care. IEEE Intell Syst, 28, 92-95. https://doi.org/10.1109/MIS.2013.51

[2]   (2018) Professionalism in Anaesthesia, Intensive Care and Pain Medicine. College of Anaesthetists of Ireland.

[3]   Lanier, W.L. (2006) A Three-Decade Perspective on Anesthesia Safety. The American Surgeon, 72, 985-989.

[4]   Knuf, K.M., Maani, C.V. and Cummings, A.K. (2018) Clinical Agreement in the American Society of Anesthesiologists Physical Status Classification. Perioper Med (Lond), 7, 14. https://doi.org/10.1186/s13741-018-0094-7

[5]   Kraev, A.I., et al. (2018) Improving the Power of the American Society of Anesthesiology Classification System to Risk Stratify Vascular Surgery Patients Based on National Surgical Quality Improvement Project-Defined Functional Status. Annals of Vascular Surgery, 52, 153-157. https://doi.org/10.1016/j.avsg.2018.04.005

[6]   Hall, W. and Pesenti, J. (2017) Growing the Artificial Intelligence Industry in the UK. Department for Digital, Culture, Media & Sport and Department for Business, Energy & Industrial Strategy. Part of the Industrial Strategy UK and the Commonwealth.

[7]   Jain, A.K., Mao, J.C. and Mohiuddin, K.M. (1996) Artificial Neural Networks: A Tutorial. Computer, 3, 31-44. https://doi.org/10.1109/2.485891

[8]   Patel, V.L., Shortliffe, E.H., Stefanelli, M., et al. (2009) The Coming of Age of Artificial Intelligence in Medicine. ArtifIntell Med, 46, 5-17. https://doi.org/10.1016/j.artmed.2008.07.017

[9]   Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning. Data Mining, Inference, and Prediction. 2nd Edition. Springer.

[10]   Khandelwal, R. (2018) Bias and Variance in Machine Learning.

[11]   Murdoch, T.B. and Detsky, A.S. (2013) The Inevitable Application of Big Data to Health Care. JAMA, 309, 1351-1352. https://doi.org/10.1001/jama.2013.393

[12]   Dilsizian, S.E. and Siegel, E.L. (2014) Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment. Curr Cardiol Rep, 16, 441. https://doi.org/10.1007/s11886-013-0441-8

[13]   Howard, R., Yin, Y.S., McCandless, L., Wang, S., Englesbe, M. and Machado-Aranda, D. (2019) Taking Control of Your Surgery: Impact of a Prehabilitation Program on Major Abdominal Surgery. J. Am. Coll. Surg, 228, 72-80. https://doi.org/10.1016/j.jamcollsurg.2018.09.018

[14]   Ridgeway, S., Wilson, J., Charlet, A., Pearson, A. and Coello, R. (2005) Infection of the Surgical Site after Arthroplasty of the Hip. J Bone Joint Surg Br, 87, 844-850. https://doi.org/10.1302/0301-620X.87B6.15121

[15]   Mahmoud, M. and Mason, K.P. (2018) Recent Advances in Intravenous Anesthesia and Anesthetics. F1000Research, 7. https://doi.org/10.12688/f1000research.13357.1

[16]   Mahmoub, M. (2018) Recent Advances in Intravenous Anesthesia and Anesthetics. PMC5911929, PMCID.

[17]   Bruhn, J., Myles, P.S., Sneyd, R. and Struys, M.M.R.F. (2006) Depth of Anaesthesia Monitoring: What’s Available, What’s Validated and What’s Next? BJA: British Journal of Anaesthesia, 97, 85-94. https://doi.org/10.1093/bja/ael120

[18]   Schnider, T.W., Minto, C.F., Gambus, P.L., Andresen, C., Goodale, D.B., Shafer, S.L. and Youngs, E.J. (1998) The Influence of Method of Administration and Covariates on the Pharmacokinetics of Propofol in Adult Volunteers. Anesthesiology, 88, 1170-1182. https://doi.org/10.1097/00000542-199805000-00006

 
 
Top