Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 7, 2021; 27(13): 1283-1295
Published online Apr 7, 2021. doi: 10.3748/wjg.v27.i13.1283
Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising
Antonio Mendoza Ladd, David L Diehl
Antonio Mendoza Ladd, Department of Internal Medicine, Division of Gastroenterology, Texas Tech University Health Sciences Center El Paso, El Paso, TX 79905, United States
David L Diehl, Department of Gastroenterology and Nutrition, Geisinger Medical Center, Danville, PA 17822, United States
Author contributions: Both Mendoza Ladd A and Diehl D participated equally in the literature search and the drafting, editing and approval of the final manuscript.
Conflict-of-interest statement: Authors declare no conflict of interest exist for this manuscript.
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:
Corresponding author: Antonio Mendoza Ladd, FACG, FASGE, Assistant Professor, Department of Internal Medicine, Division of Gastroenterology, Texas Tech University Health Sciences Center El Paso, 4800 Alberta Avenue, El Paso, TX 79905, United States.
Received: December 31, 2020
Peer-review started: December 31, 2020
First decision: January 17, 2021
Revised: January 22, 2021
Accepted: March 13, 2021
Article in press: March 13, 2021
Published online: April 7, 2021
Core Tip

Core Tip: Pancreatic adenocarcinoma is one of the deadliest malignancies in the world. Several factors are responsible for this but delayed diagnosis is one of the most important. Despite improvements in diagnostic methods, early lesions are still missed in clinical practice. Artificial intelligence (AI)-assisted diagnostic methods have the potential of improving the clinical outcomes of these patients. However, major improvements in AI technology and its implementation need to occur before potential benefits can be attained.