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February 12, 2026Gastroenterology0 citationsOpen Access

Toward Integration of Molecular Measures and Artificial Intelligence-Based Assessments With Clinical End Points in Inflammatory Bowel Disease

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WRWalter ReinischJRJens RittscherMIM Iacucci

Key Points

  • The research aims to combine molecular measures and artificial intelligence to enhance treatment assessment in IBD.
  • Analyzed tissue and blood samples from IBD patients to identify disease markers.
  • Focused on cellular and molecular correlates of disease severity and treatment response.
  • Outlined implementation strategies for using molecular descriptors in clinical settings.
  • Identified key molecular descriptors that correlate with clinical outcomes in IBD.
  • Proposed pathways for integrating artificial intelligence tools into clinical assessments.
  • Highlighted the predictive value of cellular-level healing and remission for long-term patient outcomes.

Abstract

Multimodal profiling of inflammatory bowel disease (IBD) patient tissue and blood samples has revealed the disease spectrum in unprecedented detail, and cellular and molecular correlates of disease severity and outcome in IBD have been elaborated. Incorporating these in the clinical setting would offer a unique opportunity to increase the granularity of current clinical measures and to better assess treatment response. Remission and healing at a cellular/molecular level are also likely to have predictive value for long-term outcomes. Here, we outline a path forward to implementing the most promising molecular disease descriptors as future clinical treatment targets in IBD. We focus on the concept of cellular/molecular measures of inflammation, remission, healing, response to therapy, and target pathway engagement. Monitoring mode-of-action-specific pharmacodynamic modules in the context of disease-related resolution pathways will guide assessment of novel and existing therapies. Artificial intelligence-assisted tools will be key to enabling this development, improving reproducibility, limiting costs, and delivering fast detection.

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Cite This Study

Reinisch et al. (2026) studied this question.

synapsesocial.com/papers/698d6d9f5be6419ac0d529dchttps://doi.org/10.1053/j.gastro.2025.12.002
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