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Accessing Artificial Intelligence for Clinical Decision-Making
Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of m...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521931/ https://www.ncbi.nlm.nih.gov/pubmed/34713115 http://dx.doi.org/10.3389/fdgth.2021.645232 |
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author | Giordano, Chris Brennan, Meghan Mohamed, Basma Rashidi, Parisa Modave, François Tighe, Patrick |
author_facet | Giordano, Chris Brennan, Meghan Mohamed, Basma Rashidi, Parisa Modave, François Tighe, Patrick |
author_sort | Giordano, Chris |
collection | PubMed |
description | Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US. |
format | Online Article Text |
id | pubmed-8521931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85219312021-10-27 Accessing Artificial Intelligence for Clinical Decision-Making Giordano, Chris Brennan, Meghan Mohamed, Basma Rashidi, Parisa Modave, François Tighe, Patrick Front Digit Health Digital Health Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US. Frontiers Media S.A. 2021-06-25 /pmc/articles/PMC8521931/ /pubmed/34713115 http://dx.doi.org/10.3389/fdgth.2021.645232 Text en Copyright © 2021 Giordano, Brennan, Mohamed, Rashidi, Modave and Tighe. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Giordano, Chris Brennan, Meghan Mohamed, Basma Rashidi, Parisa Modave, François Tighe, Patrick Accessing Artificial Intelligence for Clinical Decision-Making |
title | Accessing Artificial Intelligence for Clinical Decision-Making |
title_full | Accessing Artificial Intelligence for Clinical Decision-Making |
title_fullStr | Accessing Artificial Intelligence for Clinical Decision-Making |
title_full_unstemmed | Accessing Artificial Intelligence for Clinical Decision-Making |
title_short | Accessing Artificial Intelligence for Clinical Decision-Making |
title_sort | accessing artificial intelligence for clinical decision-making |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521931/ https://www.ncbi.nlm.nih.gov/pubmed/34713115 http://dx.doi.org/10.3389/fdgth.2021.645232 |
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