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20 - Epilogue: Artificial Intelligence Methods

from Section 2 - Targeting Effects

Published online by Cambridge University Press:  03 December 2019

By
Sebastián Jaramillo ,
José F. Valencia  and
Pedro L. Gambús
Edited by
Pedro L. Gambús  and
Jan F. A. Hendrickx
Pedro L. Gambús
Affiliation:
Hospital Clinic de Barcelona, Spain
Jan F. A. Hendrickx
Affiliation:
Aalst General Hospital, Belgium
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Summary

With the advances in computational technology, artificial intelligence (AI) systems have been growing exponentially and promise to become tools that are able to overcome some of the most difficult issues of medical research and patient care. Current progress in AI systems offers significant advantages in healthcare, with the potential to minimize the gap between data, knowledge and patient care. The purpose of this chapter is to examine how AI methods might affect data analysis in biomedicine and more specifically in anaesthesia. By the time this book has been published, the anaesthesia and critical care literature will be abound with manuscripts that use AI methods. It will therefore become crucial for the clinician to understand what AI is all about. A detailed understanding of AI requires an extensive knowledge of computational science and complex mathematical concepts. This chapter will provide the reader with the main insights needed to understand the basic concepts of the underlying modelling framework used by AI and will briefly review the different AI methods, their applications and limitations.

Type
Chapter
Information
Personalized Anaesthesia
Targeting Physiological Systems for Optimal Effect
 , pp. 285 - 290
Publisher: Cambridge University Press
Print publication year: 2020

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References

Further Reading

Amato, F, Lopez, A, Peña-Mendez, EM, Vaňharan Hampl, A, Havel, S: Artificial neural networks in medical diagnosis. J.Appl.Biomed. 2013; 11: 47–58.CrossRefGoogle Scholar
Hinton, G: Deep learning—a technology with the potential to transform health care. JAMA. 2018; 320: 1101–2.CrossRefGoogle ScholarPubMed
Deo, RC: Machine learning in medicine. Circulation. 2015; 132: 1920–30.Google Scholar
Chen, JH, Asch, SM: Machine learning and prediction in medicine – beyond the peak of inflated expectations. N.Engl.J.Med. 2017; 376: 2507–9.CrossRefGoogle Scholar
Doi, K: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput.Med.Imaging.Graph. 2007; 31: 198–211.CrossRefGoogle ScholarPubMed
Obermeyer, Z, Emanuel, EJ: Predicting the future – Big data, machine learning, and clinical medicine. N.Engl.J.Med. 2016; 375: 1216–19.CrossRefGoogle ScholarPubMed
Beam, AL, Kohane, IS: Big data and machine learning in health care. JAMA. 2018; 319: 1317–18.Google Scholar
Shen, D, Wu, G, Suk, HI: Deep learning in medical image analysis. Annu.Rev.Biomed.Eng. 2017; 19: 221–48.CrossRefGoogle ScholarPubMed
Stead, WW: Clinical implications and challenges of artificial intelligence and deep learning. JAMA. 2018; 320: 1107–8.Google Scholar
Zhang, Z, Beck, MW, Winkler, DA, et al: Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Ann.Transl.Med. 2018; 6: 216.Google Scholar

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