This course builds on foundational concepts to examine contemporary mechanisms driving GORD phenotypes. We revisit key epidemiological and clinical distinctions before exploring gastric physiology, including postprandial acid–bile dynamics, H. pylori-related acid output, motility patterns, obesity-related changes, and post-surgical anatomy. Oesophagogastric junction competence (LES pressure, TLESRs, hiatal hernia) and refluxate characteristics (acidic to gaseous; PPI-modified) are discussed with emphasis on symptom generation. We detail epithelial biology, micro- and macroinflammatory responses across NERD and erosive disease, and advances in nociceptive signalling, receptor expression, central modulation, and hypervigilance. Key clinical studies, cytokine-driven pathways, and emerging therapeutic approaches are highlighted. The course concludes with implications for diagnosis, phenotype-guided management, and outstanding research questions.

Building on the foundations covered in Part 1, this second course provides a deeper dive into the essential steps of guideline development. Participants will learn how to identify when a new guideline or statement is warranted, formulate focused clinical questions using PICO, and translate these into comprehensive search strategies. The course also covers the application of GRADE domains to assess evidence certainty, and guides learners through the Evidence-to-Decision framework. Finally, it clarifies the difference between good practice statements and formal guideline recommendations, ensuring participants gain the practical skills needed to contribute effectively to high-quality guideline development.

Target Audience:

• Healthcare professionals interested in developing guidelines
• Patients and stakeholders interested in understanding the process of guideline development

This comprehensive course introduces participants to the transformative role of artificial intelligence in gastroenterology. Designed for healthcare professionals, the course covers fundamental AI concepts, clinical applications, and research perspectives. Key topics include machine learning techniques, AI-assisted diagnostics, and the integration of AI systems in clinical practice. Participants will explore the benefits and challenges of AI adoption, ethical considerations, and the regulatory landscape, with an emphasis on evidence-based decision-making.

The course includes extensive PowerPoint presentations and tailored video lectures. Examples are used to demonstrate real-world applications of the material. The total duration of the course is approximately 50 minutes, with an estimated completion time of 60 minutes, including the final assessment.

This interactive course explores why optimising polyp detection is central to high-quality colonoscopy and its direct impact on morbidity and mortality. Designed for general gastroenterologists and young endoscopists, the course addresses key quality indicators, bowel preparation, scoping techniques, and the integration of AI for improved detection. Participants will learn to interpret and apply optical diagnosis systems (NICE, WASP, LST-classification, BLINC) and to choose appropriate therapeutic strategies tailored to polyp type and patient context. Surveillance intervals and outcome factors, including family history and syndromes, will be discussed in line with ESGE guidelines. Case-based discussions will focus on communication with patients regarding diagnosis and management. By combining expert testimony, videos, and practical strategies, the course aims to improve participants’ endoscopic practice, ensuring earlier detection, appropriate treatment, and better long-term outcomes.

Target Audience:

• Gastroenterologists (in training)
• Other physicians and surgeons
• Nurses
• Medical students