ReviewPLOS digital health2026
Optimizing vedolizumab therapy in ulcerative colitis: A critical synthesis of trial evidence and the emerging role of artificial intelligence.
Review in PLOS digital health, 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
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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.
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Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVedolizumab, a monoclonal antibody targeting the α4β7 integrin, offers gut-selective immunosuppression and represents a cornerstone biologic therapy for moderate-to-severe ulcerative colitis (UC). While pivotal randomized controlled trials (RCTs) have established its efficacy, a substantial subset of patients experience primary non-response. This variability presents significant clinical challenges, including patient morbidity and healthcare costs associated with cycling through ineffective therapies, underscoring an urgent need for personalized treatment strategies.
objectivesThis review aims to critically reappraise the foundational RCT evidence supporting vedolizumab use in UC, examining both strengths and limitations, and providing a comprehensive analysis of how artificial intelligence (AI), particularly machine learning (ML), can be leveraged to optimize vedolizumab treatment selection, predict outcomes, and personalize management.
methodsA systematic literature search was performed across PubMed, Scopus, and Web of Science databases. The review synthesized data from key Phase III trials (GEMINI 1, VARSITY), long-term extension safety studies, relevant meta-analyses summarizing efficacy and safety, and pertinent studies investigating the application of AI and ML techniques within inflammatory bowel disease management. The search included terms such as vedolizumab, UC, AI, and predictive modeling.
findingsLandmark trials confirmed vedolizumab's superiority over placebo for inducing and maintaining remission, with week 52 clinical remission rates reaching 41.8% in the GEMINI 1 trial. Concurrently, emerging AI/ML models, integrating complex patient data, show considerable promise in predicting biologic response with high accuracy, with some models achieving an area under the curve (AUC) of 0.82 (95% CI 0.78-0.86). Neural networks have demonstrated an accuracy of approximately 79% in specific predictive contexts.
conclusionsThe strategic integration of AI-driven predictive analytics with vedolizumab's clinical and pharmacodynamic data represents a pivotal next step towards achieving true precision medicine in UC.
Identifiers
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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.