Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies

Abstract

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

Keywords: Artificial intelligence; Gastrointestinal malignancies; Machine learning; Helicobacter pylori; State-of-the-art deep learning models

Core Tip: Application of artificial intelligence in the realm of gastrointestinal malignancies has burgeoned over the past decade as its incorporation has streamlined the work-up of gastrointestinal malignancies to address the alarming mortality statistics, largely resulting from delayed interception. The latter juxtaposed with the abundant array of contemporary diagnostic, predictive, and prognostic tools, is a testament to their underperforming status and calls for the development of digital tools that can optimize the oncologic work-up and pave the way for personalized therapies.