2nd Edition of International Conference on Gastroenterology
September 24-26, 2026, London, UK
AI, Digital Health & Data Science for GI Care
Artificial intelligence, digital health, and data science are increasingly integrated into gastroenterology, offering powerful tools to enhance diagnosis, treatment, and healthcare delivery. AI-driven systems improve lesion detection in endoscopy, optimize workflow efficiency, and support clinical decision-making. Digital health platforms enable telemedicine, remote monitoring, and patient-reported outcome tracking, particularly for chronic GI diseases. Data science allows large-scale analysis of clinical, imaging, and molecular data to identify patterns and predict outcomes. Ethical considerations, data quality, and clinician oversight remain critical for safe implementation. This topic highlights current applications, challenges, and future opportunities for AI and digital innovation in advancing gastrointestinal care.
Gilles R G Monif
University of Florida and Infectious Diseases Incorporated, United States
Tracy E Hill
Ph.D. & Associates LLC, United States
John Andrew Sutton
Invention City, United States
Neha V Patel
UT Southwestern Medical Center, United States
Radhika Patel
Croydon Health Trust, United Kingdom
Aya Fares
Colchester Hospital, United Kingdom
Moses G Ogundipe
University of Buckingham Medical School, United KingdomSubmit your abstract Today
Title : Gut mucosal immunity and inflammatory bowel disease
Youcun Qian, University of Chinese Academy of Sciences, China
Title : Early and late evaluation of esophageal mucosectomy in surgical treatment of advanced esophageal achalasia
Jose Luis Braga De Aquino, Pontifical Catholic University of Campinas, Brazil
Title : The efficacy of fermented foods as an adjunct therapy in ulcerative colitis - a literature review
Jack D Doorly, Alfred Health, Australia
Title : Professional dining and health
Yaffa Machnes, Bar-Ilan University, Israel
Title : How epigastric impedance would radically change gastric medicine
John Andrew Sutton, Invention City, United States
Title : Differentiating simple and aggressive phenotypes of metabolic dysfunction-associated steatotic liver disease utilizing bioelectrical impedance-derived metabolic age gap
Yury Marakhouski, Belarusian State Medical University, Belarus