Evidence map›Paper›PMID 38411898›Full record

ReviewCurrent gastroenterology reports2024

Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?

Mehwish Ahmed, Molly L Stone, Ryan W Stidham

Abstract readReview
In one paragraph

Review in Current gastroenterology reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Therapeutic Decisions in Inflammatory Bowel Disease: ChatGPT-4.0 Compared to Medical Expertise.JGH open : an open access journal of gastroenterology and hepatology · 2026
    Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Raising the bar in ulcerative colitis management.Therapeutic advances in gastroenterology · 2024
    Review
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

3 authors.

Mehwish AhmedDivision of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-9500-3401
Molly L StoneDivision of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-0663-0092
Ryan W StidhamDivision of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, MI, USA. ryanstid@med.umich.edu.ORCID http://orcid.org/0000-0001-9638-2186

Funding

Automated Measurement of Bowel Damage Using Enterography Imaging to Predict Clinical Outcomes in Crohn’s Disease.R01DK124779 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STIDHAM, RYAN WILLIAM · 2020 to 2023
$2.1M
NIDDK NIH HHS R01 DK124779NIDDK NIH HHS R01DK124779
6 · The paper itself

Abstract

purpose of reviewArtificial intelligence (AI) is quickly demonstrating the ability to address problems and challenges in the care of IBD. This review with commentary will highlight today's advancements in AI applications for IBD in image analysis, understanding text, and replicating clinical knowledge and experience. RECENT

findingsAdvancements in machine learning methods, availability of high-performance computing, and increasing digitization of medical data are providing opportunities for AI to assist in IBD care. Multiple groups have demonstrated the ability of AI to replicate expert endoscopic scoring in IBD, with expansion into automated capsule endoscopy, enterography, and histologic interpretations. Further, AI image analysis is being used to develop new endoscopic scoring with more granularity and detail than is possible using conventional methods. Advancements in natural language processing are proving to reduce laborious tasks required in the care of IBD, including documentation, information searches, and chart review. Finally, large language models and chatbots that can understand language and generate human-like replies are beginning to exhibit clinical intelligence that will revolutionize how we deliver IBD care. Today, AI is being deployed to replicate expert judgement in specific tasks where disagreement, subjectivity, and bias are common. However, the near future will herald contributions of AI doing what we cannot, including new detailed measures of IBD, enhanced analysis of images, and perhaps even fully automating care. As we speculate on future technologic capabilities that may improve how we care for IBD, this review will also consider how we will implement and fairly use AI in practice.

Indexed as

Artificial IntelligenceInflammatory Bowel DiseasesHumansMachine LearningNatural Language ProcessingArtificial intelligenceClinical trialsCrohn’s diseaseDecision supportEndoscopic scoringInflammatory bowel diseaseMachine learningPrediction modelsSegmentationUlcerative colitis

Identifiers

PMID38411898
PMCPMC11320710

What OpenQuestion holds

Textmetadata
LicenceTDM
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.