Sridhar GR, Siva Prasad AV, Lakshmi G. Scope and caveats: Artificial intelligence in gastroenterology. Artif Intell Gastroenterol 2024; 5(1): 91607 [DOI: 10.35712/aig.v5.i1.91607]
Corresponding Author of This Article
Gumpeny Ramachandra Sridhar, FRCP (Hon), MD, Consultant Physician-Scientist, Department of Endocrinology and Diabetes, Endocrine and Diabetes Centre, 15-12-15 Krishnanagar, Visakhapatnam 530002, India. grsridhar@hotmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Scope and caveats: Artificial intelligence in gastroenterology
Gumpeny Ramachandra Sridhar, Atmakuri V Siva Prasad, Gumpeny Lakshmi
Gumpeny Ramachandra Sridhar, Department of Endocrinology and Diabetes, Endocrine and Diabetes Centre, Visakhapatnam 530002, India
Atmakuri V Siva Prasad, Department of Gastroenterology, Institute of Gastroenterology, Visakhapatnam 530003, India
Gumpeny Lakshmi, Department of Internal Medicine, Gayatri Vidya Parishad Institute of Healthcare & Medical Technology, Visakhapatnam 530048, India
Author contributions: All three authors contributed to this manuscript; Sridhar GR designed the concept and outline; Siva Prasad AV provided inputs to the discussion and design; Lakshmi G contributing to the writing and editing of the manuscript.
Conflict-of-interest statement: The authors declare no conflict of interest.
Open-Access: 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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Gumpeny Ramachandra Sridhar, FRCP (Hon), MD, Consultant Physician-Scientist, Department of Endocrinology and Diabetes, Endocrine and Diabetes Centre, 15-12-15 Krishnanagar, Visakhapatnam 530002, India. grsridhar@hotmail.com
Received: January 23, 2024 Peer-review started: January 23, 2024 First decision: February 6, 2024 Revised: February 18, 2024 Accepted: March 29, 2024 Article in press: March 29, 2024 Published online: April 30, 2024 Processing time: 96 Days and 20.4 Hours
Abstract
The use of Artificial intelligence (AI) has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.
Core Tip: Artificial intelligence helps in the early identification, management and prognostication of gastrointestinal diseases through applications in endoscopy and histopathological interpretation. Proof of concept studies exist for all of these, but need validation by randomized clinical trials before they can be incorporated into clinical work flow.
