Evidence map›Paper›PMID 39996136›Full record

ReviewTherapeutic advances in gastroenterology2025

Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review.

Rocio Sedano, Virginia Solitano, Sudheer K Vuyyuru, Yuhong Yuan, Jurij Hanžel, Christopher Ma, Olga Maria Nardone, Vipul Jairath

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Therapeutic advances in gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed.

  1. Review
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  16. From AI-AssistedPharmaceuticals (Basel, Switzerland) · 2025
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Rocio SedanoDivision of Gastroenterology, Department of Medicine, Western University, London, ON, Canada.ORCID https://orcid.org/0000-0002-3311-638X
Virginia SolitanoDepartment of Epidemiology and Biostatistics, Western University, London, ON, Canada.ORCID https://orcid.org/0000-0002-2935-6778
Sudheer K VuyyuruDivision of Gastroenterology, Department of Medicine, Western University, London, ON, Canada.
Yuhong YuanDivision of Gastroenterology, Department of Medicine, Western University, London, ON, Canada.
Jurij HanželDepartment of Gastroenterology, University Medical Centre Ljubljana, University of Ljubljana, Ljubljana, Slovenia.
Christopher MaDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.ORCID https://orcid.org/0000-0002-4698-9948
Olga Maria NardoneGastroenterology, Department of Public Health, University Federico II of Naples, Naples, Italy.
Vipul JairathDivision of Gastroenterology, Department of Medicine, Western University, London, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating artificial intelligence (AI) into clinical trials for inflammatory bowel disease (IBD) has potential to be transformative to the field. This article explores how AI-driven technologies, including machine learning (ML), natural language processing, and predictive analytics, have the potential to enhance important aspects of IBD trials-from patient recruitment and trial design to data analysis and personalized treatment strategies. As AI advances, it has potential to improve long-standing challenges in trial efficiency, accuracy, and personalization with the goal of accelerating the discovery of novel therapies and improve outcomes for people living with IBD. AI can streamline multiple trial phases, from target identification and patient recruitment to data analysis and monitoring. By integrating multi-omics data, electronic health records, and imaging repositories, AI can uncover molecular targets and personalize trial strategies, ultimately expediting drug development. However, the adoption of AI in IBD clinical trials encounters significant challenges. These include technical barriers in data integration, ethical concerns regarding patient privacy, and regulatory issues related to AI validation standards. Additionally, AI models risk producing biased outcomes if training datasets lack diversity, potentially impacting underrepresented populations in clinical trials. Addressing these limitations requires standardized data formats, interdisciplinary collaboration, and robust ethical frameworks to ensure inclusivity and accuracy. Continued partnerships among clinicians, researchers, data scientists, and regulators will be essential to establish transparent, patient-centered AI frameworks. By overcoming these obstacles, AI has the potential to enhance the efficiency, equity, and efficacy of IBD clinical trials, ultimately benefiting patient care.

Indexed as

artificial intelligence (AI)clinical trialsinflammatory bowel diseasemachine learning (ML)patient recruitment

Identifiers

PMID39996136
PMCPMC11848901

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.