Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!

Abstract

Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) and machine learning (ML) applications. In this opinion review, we initially elaborate on the current status of the application of AI/ML-based software in gastroenterology. Currently, AI/ML-based models have been developed in the following applications: Models integrated into the clinical setting following real-time patient data flagging patients at high risk for developing a gastrointestinal disease, models employing non-invasive parameters that provide accurate diagnoses aiming to either replace, minimize, or refine the indications of endoscopy, models utilizing genomic data to diagnose various gastrointestinal diseases, computer-aided diagnosis systems facilitating the interpretation of endoscopy images, models to facilitate treatment allocation and predict the response to treatment, and finally, models in prognosis predicting complications, recurrence following treatment, and overall survival. Then, we elaborate on several challenges and how they may negatively impact the widespread application of AI in healthcare and gastroenterology. Specifically, we elaborate on concerns regarding accuracy, cost-effectiveness, cybersecurity, interpretability, oversight, and liability. While AI is unlikely to replace physicians, it will transform the skillset demanded by future physicians to practice. Thus, physicians are expected to engage with AI to avoid becoming obsolete.

Keywords: Artificial intelligence, Machine learning, Gastroenterology, Cost-effectiveness, Interpretability, Accuracy

Core Tip: Currently, artificial intelligence (AI) and machine learning (ML) have several applications in the prevention, diagnosis, treatment, and prognosis of various gastrointestinal diseases, including gastroesophageal reflux disease, esophageal cancer, gastric cancer, gastrointestinal bleeding, inflammatory bowel diseases, polyps, colorectal cancer, and others. Despite their promising results, AI/ML applications in gastroenterology are hindered by several challenges, including accuracy, cost-effectiveness, cybersecurity, interpretability, oversight, and liability concerns. In this opinion review, we elaborate on these challenges and present different ways to overcome them.