Evidence map›Paper›PMID 39243338›Full record

ReviewCurrent gastroenterology reports2024

Generative AI in Pediatric Gastroenterology.

John M Rosen

Abstract readReview
PubMed Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

John M RosenUniversity of Arkansas for Medical Sciences, Little Rock, AR, USA. jrosen@uams.edu.ORCID http://orcid.org/0000-0003-4291-3214

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThe integration of digital technology into medical practice is often thrust upon clinicians, with standards and routines developed long after initiation. Clinicians should endeavor towards a basic understanding even of emerging technologies so that they can direct its use. The intent of this review is to describe the current state of rapidly evolving generative artificial intelligence (GAI), and to explore both how pediatric gastroenterology practice may benefit as well as challenges that will be faced. RECENT

findingsAlthough little research demonstrating the acceptance, practice, and outcomes associated with GAI in pediatric gastroenterology is published, there are relevant data adjacent to the specialty and overwhelming potential as professed in the media. Best practice guidelines are widely developed in academic publishing and resources to initiate and improve practical user skills are prevalent. Initial published evidence supports broad acceptance of the technology as part of medical practice by clinicians and patients, describes methods with which higher quality GAI can be developed, and identifies the potential for bias and disparities resulting from its use. GAI is broadly available as a digital tool for incorporation into medical practice and holds promise for improved quality and efficiency of care, but investigation into how GAI can best be used remains at an early stage despite rapid evolution of the technology.

Indexed as

Artificial IntelligenceGastroenterologyPediatricsChildHumansAI chatbotArtificial intelligenceDigital technologyGenerative AIPediatric gastroenterology

Identifiers

What OpenQuestion holds

Textmetadata
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.