Improving the Treatment Response of Patients with Irritable Bowel Syndrome: Implementing a Second-Generation Artificial Intelligence System for Overcoming Resistance

Abstract

Irritable bowel syndrome (IBS) is a common functional disorder. The syndrome’s multifactorial pathophysiology makes it challenging to design effective therapies. The present paper reviews several therapeutic approaches to treating IBS, highlighting the challenges of losing response over time to therapies. Here, we present the relevance of chronobiology in biological systems focusing on the potential of chronotherapy for IBS. Artificial intelligence- (AI-) based approaches have been developed over the last few years to improve the diagnosis, therapeutic approaches, and monitoring of patients with various diseases. We discuss the use of first-generation AI platforms and their limitations in clinical practice and present the establishment of a second-generation system designed to overcome obstacles in managing these patients. The system identifies costly patients and those who do not respond to therapies and may benefit from algorithm-based therapies. We present a patient-tailored approach for improving the response to therapy in IBS using an AI-based algorithm. This system provides a tool for a patient-tailored monitoring system. The second-generation AI system can provide a comprehensive tool for improving the diagnosis and therapy and monitoring of patients with IBS.