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REVIEW article

Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/frai.2024.1446693
This article is part of the Research Topic AI in Digital Oncology: Imaging and Wearable Technology for Cancer Detection and Management View all 4 articles

Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers

Provisionally accepted
Zahra Shokati Eshkiki Zahra Shokati Eshkiki 1*Abolfazl Akbari Abolfazl Akbari 2Dr. Maryam Adabi Dr. Maryam Adabi 1Mohsem Masoodi Mohsem Masoodi 2Abolfazl Namazi Abolfazl Namazi 2Fatemeh Mansouri Fatemeh Mansouri 3Seidamir Pasha Tabaeian Seidamir Pasha Tabaeian 2
  • 1 Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
  • 2 Iran University of Medical Sciences, Tehran, Tehran, Iran
  • 3 Islamic Azad University, Ramsar Branch, Iran

The final, formatted version of the article will be published soon.

    One of the foremost causes of global healthcare burden is cancer of the gastrointestinal tract. The medical records, lab results, radiographs, endoscopic images, tissue samples, and medical histories of patients with gastrointestinal malignancies provide an enormous amount of medical data. There are encouraging signs that the advent of artificial intelligence could enhance the treatment of gastrointestinal issues with this data. Deep learning algorithms can swiftly and effectively analyze unstructured, high-dimensional data, including texts, images, and waveforms, while advanced machine learning approaches could reveal new insights into disease risk factors and phenotypes. In summary, artificial intelligence has the potential to revolutionize various features of gastrointestinal cancer care, such as early detection, diagnosis, therapy, and prognosis. This paper highlights some of the many potential applications of artificial intelligence in this domain. Additionally, we discuss the present state of the discipline and its potential future developments.

    Keywords: artificial intelligence, Gastrointestinal cancers, machine learning, deep learning, Early detection, diagnosis, treatment response, Survival Prediction

    Received: 10 Jun 2024; Accepted: 02 Dec 2024.

    Copyright: © 2024 Shokati Eshkiki, Akbari, Adabi, Masoodi, Namazi, Mansouri and Tabaeian. 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) or licensor 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.

    * Correspondence: Zahra Shokati Eshkiki, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.