Artificial intelligence in inflammatory bowel disease: bridging innovation, implementation and impact

Area of Science:

• * Advancing medical informatics and computational biology in gastroenterology.
• * Leveraging artificial intelligence for complex disease management.

Background:

• * Artificial intelligence (AI) is increasingly impacting inflammatory bowel disease (IBD) care beyond initial applications.
• * Next-generation AI systems offer deeper disease insights, personalized treatments, and improved clinical workflows.

Purpose of the Study:

• * To trace the evolution of AI in IBD from specific tools to comprehensive platforms for precision medicine.
• * To highlight validated AI applications in IBD diagnosis, monitoring, outcome prediction, and neoplasia surveillance.
• * To explore stakeholder expectations and address challenges for AI integration in IBD management.

Main Methods:

• * Review and synthesis of current AI advancements in IBD, including multimodal data integration (endo-histo-omics).
• * Analysis of AI applications in intestinal barrier assessment, remote monitoring with wearables, and large language models for clinical support.
• * Examination of stakeholder perspectives and discussion of implementation barriers.

Main Results:

• * AI is evolving into foundational platforms supporting data-driven precision medicine in IBD.
• * Validated AI tools exist for diagnosis, monitoring, outcome prediction, and neoplasia surveillance.
• * Key challenges include explainability, workflow integration, reimbursement, and sustainability.

Conclusions:

• * AI has the potential to redefine IBD care through ethical and clinically aligned innovation.
• * Collaboration, transparency, and responsible implementation are crucial for AI's future in IBD.
• * AI promises a new era of personalized, efficient, and equitable care for IBD patients.