Next-generation AI in the Management of GI Patients

Recent advances in artificial intelligence (AI) are rapidly moving beyond narrowly defined, task-specific applications towards generalizable and context-aware systems that can support longitudinal patient care. This paradigm shift in AI is unfolding simultaneously in the field of gastroenterology against a backdrop of increasingly rich data from various sources, including endoscopy, bio signals, omics, and electronic health records. This Research Topic delves into the development of next-generation AI systems focused on moving beyond simple detection and classification towards clinically embedded, holistically intelligent systems. Of particular importance will be the exploration of: Large Language Models (LLMs), AI agents, and clinical foundation models for integrating relational and non-relational data; AI systems capable of reasoning across time, modalities, and patient trajectories; Human-in-the-loop (HITL) and Human-on-the-loop (HOTL) frameworks designed to assist gastroenterologists with complex decision-making tasks; and emergent AI paradigms such as post-multimodal learning, active learning, nested or federated learning, neuro-symbolic AI, and quantum-inspired optimization techniques. Special emphasis will be placed on gastrointestinal (GI) endoscopy, a field in which AI applications, particularly convolutional ones, have found extensive use and have fully entered clinical practice.Consideration of the real-world constraints, including regulatory alignment (such as with the EU AI Act), data governance under the FAIR principles, and clinical deployment strategies, will be highly encouraged. Through its exploration of advanced computational techniques alongside foundational gastroenterology practice, this issue intends to further advance the role of AI in gastrointestinal diagnostics, monitoring, and therapeutic decision-making.The objective of this Research Topic is to develop and consolidate clinically relevant AI that can: Integrate multiple data types (imaging, signals, text and clinical history) from patients into singular decision-making systems; Leverage LLMs and foundation models for a broad range of clinical reasoning, reporting and decision support applications; Allow continuous monitoring and prediction of individual patient trajectories through the use of bio signals and data collected by wearables; Provide real-time support for advanced endoscopic procedures, encourage compliance with FAIR data principles and evolving regulatory frameworks such as the AI Act; Employ advanced computational paradigms such as quantum-inspired methods for improved optimization and better quantification of uncertainty.This Research Topic seeks high-quality contributions that are focused on the clinical relevance and impact on patient outcomes, while contributing to the advancement of AI-driven management of GI diseases. Relevant areas include (but are not limited to): - Foundation Models; AI agents, and LLMs in gastroenterology- AI for longitudinal patient management with data including different types of endoscopy, biomedical signals, electronic health records, and wearable integration. - FAIR, Trustworthy, and or Regulatory-Compliant AI for gastroenterologyWe welcome original research papers, brief research reports, systematic reviews, comprehensive narrative reviews, methods, perspectives, data descriptors, perspectives, editorial, clinical trials, general commentary, opinion, mini review, meta review, study protocols, and case series emphasizing real-world applicability, interdisciplinary collaboration, and rigorous validation on diverse and well-curated datasets in gastroenterology. Topic editors: Raffaele Pellegrino, Antonietta G Gravina, Palak Handa, Sri Krishnan Source: https://www.frontiersin.org/research-topics/80336/next-generation-ai-in-the-management-of-gi-patients