Artificial intelligence in gastric cancer: a translational narrative review.

Content Provider	Europe PMC
Author	Yu, Chaoran Helwig, Ernest Johann
Copyright Year	2021
Abstract	Increasing clinical contributions and novel techniques have been made by artificial intelligence (AI) during the last decade. The role of AI is increasingly recognized in cancer research and clinical application. Cancers like gastric cancer, or stomach cancer, are ideal testing grounds to see if early undertakings of applying AI to medicine can yield valuable results. There are numerous concepts derived from AI, including machine learning (ML) and deep learning (DL). ML is defined as the ability to learn data features without being explicitly programmed. It arises at the intersection of data science and computer science and aims at the efficiency of computing algorithms. In cancer research, ML has been increasingly used in predictive prognostic models. DL is defined as a subset of ML targeting multilayer computation processes. DL is less dependent on the understanding of data features than ML. Therefore, the algorithms of DL are much more difficult to interpret than ML, even potentially impossible. This review discussed the role of AI in the diagnostic, therapeutic and prognostic advances of gastric cancer. Models like convolutional neural networks (CNNs) or artificial neural networks (ANNs) achieved significant praise in their application. There is much more to be fully covered across the clinical administration of gastric cancer. Despite growing efforts, adapting AI to improving diagnoses for gastric cancer is a worthwhile venture. The information yield can revolutionize how we approach gastric cancer problems. Though integration might be slow and labored, it can be given the ability to enhance diagnosing through visual modalities and augment treatment strategies. It can grow to become an invaluable tool for physicians. AI not only benefits diagnostic and therapeutic outcomes, but also reshapes perspectives over future medical trajectory.
ISSN	23055839
Volume Number	9
PubMed Central reference number	PMC7940908
Issue Number	3
PubMed reference number	33708896
Journal	Annals of Translational Medicine [Ann Transl Med]
e-ISSN	23055847
DOI	10.21037/atm-20-6337
Language	English
Publisher	AME Publishing Company
Publisher Date	2021-02-01
Access Restriction	Open
Rights License	Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0. 2021 Annals of Translational Medicine. All rights reserved.
Subject Keyword	Artificial intelligence (AI) endoscope convolutional neural networks (CNNs) gastric cancer genomics
Content Type	Text
Resource Type	Article
Subject	Medicine