Evidence map›Paper›PMID 40831567›Full record

ReviewFrontiers in immunology2025

Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.

Diletta De Deo, Arianna Dal Buono, Roberto Gabbiadini, Olga Maria Nardone, Rocio Ferreiro-Iglesias, Giuseppe Privitera, Cristiana Bonifacio, Manuel Barreiro-de Acosta, Cristina Bezzio, Alessandro Armuzzi

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

10 authors.

Diletta De DeoIBD Center, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Milan, Italy.
Arianna Dal BuonoIBD Center, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Milan, Italy.
Roberto GabbiadiniIBD Center, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Milan, Italy.
Olga Maria NardoneGastroenterology, Department of Public Health, University of Naples Federico II, Naples, Italy.
Rocio Ferreiro-IglesiasGastroenterology Department, Hospital Clinico Universitario de Santiago de Compostela, Santiago de Compostela, Spain.
Giuseppe PriviteraIBD Center, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Milan, Italy.
Cristiana BonifacioRadiology Department, IRCCS Humanitas Research Hospital, Milan, Italy.
Manuel Barreiro-de AcostaGastroenterology Department, Hospital Clinico Universitario de Santiago de Compostela, Santiago de Compostela, Spain.
Cristina BezzioIBD Center, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Milan, Italy.
Alessandro ArmuzziIBD Center, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: Artificial intelligence (AI) is rapidly gaining traction in gastroenterology, particularly in the management of inflammatory bowel disease (IBD). Given the complexity of IBD care, AI offers the potential to enhance diagnosis, monitoring, and treatment. This review aims to summarize recent developments in AI applications for IBD and identify key challenges and opportunities for future research and clinical implementation. Methods: A narrative literature review was conducted, incorporating recent studies utilizing AI -including machine learning (ML) and deep learning (DL) - across various aspects of IBD care. Results: AI has demonstrated utility in multiple domains of IBD management, including endoscopic disease activity assessment, histological evaluation, imaging interpretation, prediction of disease course, treatment response, and real-world data integration. Despite promising accuracy and utility, most models remain in early development stages and lack widespread clinical validation. Major barriers include data heterogeneity, limited generalizability, and regulatory uncertainties. Conclusion: AI has significant potential to revolutionize IBD care. Continued multidisciplinary collaboration, validation in diverse clinical settings, and integration into clinical workflows are critical for realizing its full impact.

Indexed as

Artificial IntelligenceBiomarkersInflammatory Bowel DiseasesPrecision MedicineDeep LearningDisease ManagementHumansMachine LearningBiomarkersAPR: algorithm-predicted remissionartificial intelligenceBPNN: back-propagation neural networkCART: classification and regression treesdigital biomarkersinflammatory bowel diseasemachine learningpersonalized medicine artificial neural network

Identifiers

PMID40831567
PMCPMC12358455

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.