Evidence map›Paper›PMID 42085653›Full record

ArticleJMIR bioinformatics and biotechnology2026

Readability of AI-Generated Patient Information on Glucagon-Like Peptide-1 Receptor Agonists.

Tyler Williams, Ines Bilic-Curcic, Jonathan Hurley, Harisankeerth Mummareddy, Maja Cigrovski Berkovic, Silvija Canecki Varzic, Marina Gradiser

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR bioinformatics and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Tyler WilliamsFaculty of Medicine, Tulane University, New Oreleans, LA, United States.ORCID http://orcid.org/0009-0007-9248-0206
Ines Bilic-CurcicFaculty of Medicine, Tulane University, New Oreleans, LA, United States.ORCID http://orcid.org/0000-0002-8861-5987
Jonathan HurleySchool of Medicine, Tulane University, New Orelans, LA, United States.ORCID http://orcid.org/0009-0000-0365-5123
Harisankeerth MummareddyUniversity of Tennessee Health Science Center, Menphis, TN, United States.ORCID http://orcid.org/0009-0004-8242-4786
Maja Cigrovski BerkovicFaculty of Kinesiology, University of Zagreb, Horvacanski zavoj 15, Zagreb, 10000, Croatia, 385 915128167.ORCID http://orcid.org/0000-0003-0750-9785
Silvija Canecki VarzicFaculty of Dental Medicine and Health, University of Osijek, Osijek, County of Osijek-Baranja, Croatia.ORCID http://orcid.org/0000-0001-9535-7915
Marina GradiserFaculty of Medicine, University of Split, Split, Split-Dalmatia, Croatia.ORCID http://orcid.org/0000-0002-6308-1384

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Artificial intelligence (AI)-generated content on glucagon-like peptide-1 receptor agonists (GLP-1RAs) gave informationally detailed responses, but its readability remains suboptimal for many patients. Incorporating literacy-sensitive design principles into AI health communication is essential to ensure equitable access to digital medical information.

Indexed as

AIartificial intelligenceChatGPTGLP-1RAGoogle Geminihealth literacyreadability

Identifiers

PMID42085653
PMCPMC13143154

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.