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Artificial intelligence in perioperative management of major gastrointestinal surgeries
Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the av...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173379/ https://www.ncbi.nlm.nih.gov/pubmed/34135552 http://dx.doi.org/10.3748/wjg.v27.i21.2758 |
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author | Solanki, Sohan Lal Pandrowala, Saneya Nayak, Abhirup Bhandare, Manish Ambulkar, Reshma P Shrikhande, Shailesh V |
author_facet | Solanki, Sohan Lal Pandrowala, Saneya Nayak, Abhirup Bhandare, Manish Ambulkar, Reshma P Shrikhande, Shailesh V |
author_sort | Solanki, Sohan Lal |
collection | PubMed |
description | Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called “big data” to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery. |
format | Online Article Text |
id | pubmed-8173379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-81733792021-06-15 Artificial intelligence in perioperative management of major gastrointestinal surgeries Solanki, Sohan Lal Pandrowala, Saneya Nayak, Abhirup Bhandare, Manish Ambulkar, Reshma P Shrikhande, Shailesh V World J Gastroenterol Minireviews Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called “big data” to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery. Baishideng Publishing Group Inc 2021-06-07 2021-06-07 /pmc/articles/PMC8173379/ /pubmed/34135552 http://dx.doi.org/10.3748/wjg.v27.i21.2758 Text en ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
spellingShingle | Minireviews Solanki, Sohan Lal Pandrowala, Saneya Nayak, Abhirup Bhandare, Manish Ambulkar, Reshma P Shrikhande, Shailesh V Artificial intelligence in perioperative management of major gastrointestinal surgeries |
title | Artificial intelligence in perioperative management of major gastrointestinal surgeries |
title_full | Artificial intelligence in perioperative management of major gastrointestinal surgeries |
title_fullStr | Artificial intelligence in perioperative management of major gastrointestinal surgeries |
title_full_unstemmed | Artificial intelligence in perioperative management of major gastrointestinal surgeries |
title_short | Artificial intelligence in perioperative management of major gastrointestinal surgeries |
title_sort | artificial intelligence in perioperative management of major gastrointestinal surgeries |
topic | Minireviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173379/ https://www.ncbi.nlm.nih.gov/pubmed/34135552 http://dx.doi.org/10.3748/wjg.v27.i21.2758 |
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