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Chapter 22 - New Frontiers in Neuroprognostication: Machine Learning and AI

from Part II - Other Topics in Neuroprognostication

Published online by Cambridge University Press:  14 November 2024

By
Charlene Ong  and
Matthew Miller
Edited by
David M. Greer  and
Neha S. Dangayach
David M. Greer
Affiliation:
Boston University School of Medicine and Boston Medical Center
Neha S. Dangayach
Affiliation:
Icahn School of Medicine at Mount Sinai and Mount Sinai Health System
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Summary

Artificial intelligence (AI) refers to a wide range of computational methods that approximate human reasoning. Machine learning is a subclass of AI that uses predictive computer models that adjust and improve their performance after exposure to data.[1–3] Machine learning is increasingly used for various purposes, including facial recognition, financial strategy, automated vehicles, and medical applications.[2,3] While objections to AI stem both from skepticism that automation can approach human reasoning and fears of obsolescence, a basic understanding of AI methods, uses, and limitations will be increasingly important as it continues to weave itself into the fabric of our society.

What contribution can machine learning offer the field of neuroprognostication? Certainly, AI approaches hold great promise in advancing our pathophysiological understanding of neurological injury, improving the accuracy of prognostication for patients and families, and streamlining clinical workflows.

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Information
Neuroprognostication in Critical Care , pp. 305 - 319
Publisher: Cambridge University Press
Print publication year: 2024

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