ArticleCrohn's & colitis 3602026
Agentic artificial intelligence in inflammatory bowel disease: toward autonomous and adaptive care.
Article in Crohn's & colitis 360, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inflammatory bowel disease (IBD) is a chronic, heterogeneous condition requiring ongoing monitoring and iterative therapeutic adjustment. Current management relies on multiple clinical, biochemical, imaging, and molecular data sources that are collected intermittently and rarely integrated into a unified representation of disease activity, leading to reactive clinical decision-making. Although artificial intelligence (AI) has demonstrated promise in IBD, most applications remain task-specific and operate on data collected at isolated time points, failing to capture the longitudinal nature of disease. Agentic AI offers a shift toward continuous and adaptive care by integrating diverse data streams into an evolving representation of disease state. This enables early detection of change and supports proactive intervention through a closed-loop system linking monitoring, interpretation, and action. Despite its potential, challenges related to data quality, interpretability, and clinical integration must be addressed for implementation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.