Evidence map›Paper›PMID 38186861›Full record

ReviewWorld journal of gastroenterology2023

Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!

Chrysanthos D Christou, Georgios Tsoulfas

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in World journal of gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.7field-weighted citation impact, top 6% of its field
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

12 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Acute pancreatitis: Translating early mechanisms to bedside management.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2025
    Review
  8. Review
  9. Article
  10. Artificial Intelligence in Upper Gastrointestinal Diagnosis.The Korean journal of helicobacter and upper gastrointestinal research · 2025
    Review
  11. Review
  12. 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

2 authors at 1 institution in 1 country.

Chrysanthos D ChristouDepartment of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece.
Georgios TsoulfasDepartment of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece.
Aristotle University of Thessaloniki · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) and machine learning (ML) applications. In this opinion review, we initially elaborate on the current status of the application of AI/ML-based software in gastroenterology. Currently, AI/ML-based models have been developed in the following applications: Models integrated into the clinical setting following real-time patient data flagging patients at high risk for developing a gastrointestinal disease, models employing non-invasive parameters that provide accurate diagnoses aiming to either replace, minimize, or refine the indications of endoscopy, models utilizing genomic data to diagnose various gastrointestinal diseases, computer-aided diagnosis systems facilitating the interpretation of endoscopy images, models to facilitate treatment allocation and predict the response to treatment, and finally, models in prognosis predicting complications, recurrence following treatment, and overall survival. Then, we elaborate on several challenges and how they may negatively impact the widespread application of AI in healthcare and gastroenterology. Specifically, we elaborate on concerns regarding accuracy, cost-effectiveness, cybersecurity, interpretability, oversight, and liability. While AI is unlikely to replace physicians, it will transform the skillset demanded by future physicians to practice. Thus, physicians are expected to engage with AI to avoid becoming obsolete.

Indexed as

Artificial IntelligenceGastroenterologyComputer SecurityHumansMachine LearningSoftwareAccuracyArtificial intelligenceCost-effectivenessGastroenterologyInterpretabilityMachine learning

Identifiers

PMID38186861
PMCPMC10768398
OpenAlexW4390246620

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.