Evidence map›Paper›PMID 39814338›Full record

ArticleJournal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases2025

Evaluation of the Reliability of ChatGPT to Provide Guidance on Recombinant Zoster Vaccination for Patients With Rheumatic and Musculoskeletal Diseases.

Akhil Sood, Amanda Moyer, Pegah Jahangiri, Diane Mar, Prachaya Nitichaikulvatana, Nitya Ramreddy, Liya Stolyar, Janice Lin

Abstract read
In one paragraph

Article in Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases, 2025. 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. Article
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

8 authors.

Akhil SoodFrom the Division of Immunology and Rheumatology, Stanford University School of Medicine, Palo Alto, CA.ORCID 0000-0001-6313-2738
Amanda Moyer
Pegah Jahangiri
Diane Mar
Prachaya Nitichaikulvatana
Nitya Ramreddy
Liya Stolyar
Janice Lin

Funding

Training Program in Adult and Pediatric RheumatologyT32AR050942 · NIAMS · STANFORD UNIVERSITY · PI LEWIS, DAVID BRAM, ROBINSON, WILLIAM H · 2005 to 2025
$6.6M
NIAMS NIH HHS T32 AR050942
6 · The paper itself

Abstract

introductionLarge language models (LLMs) such as ChatGPT can potentially transform the delivery of health information. This study aims to evaluate the accuracy and completeness of ChatGPT in responding to questions on recombinant zoster vaccination (RZV) in patients with rheumatic and musculoskeletal diseases.

methodsA cross-sectional study was conducted using 20 prompts based on information from the Centers for Disease Control and Prevention (CDC), the Advisory Committee on Immunization Practices (ACIP), and the American College of Rheumatology (ACR). These prompts were inputted into ChatGPT 3.5. Five rheumatologists independently scored the ChatGPT responses for accuracy (Likert 1 to 5) and completeness (Likert 1 to 3) compared with validated information sources (CDC, ACIP, and ACR).

resultsThe overall mean accuracy of ChatGPT responses on a 5-point scale was 4.04, with 80% of responses scoring ≥4. The mean completeness score of ChatGPT response on a 3-point scale was 2.3, with 95% of responses scoring ≥2. Among the 5 raters, ChatGPT unanimously scored with high accuracy and completeness to various patient and physician questions surrounding RZV. There was one instance where it scored with low accuracy and completeness. Although not significantly different, ChatGPT demonstrated the highest accuracy and completeness in answering questions related to ACIP guidelines compared with other information sources.

conclusionsChatGPT exhibits promising ability to address specific queries regarding RZV for rheumatic and musculoskeletal disease patients. However, it is essential to approach ChatGPT with caution due to risk of misinformation. This study emphasizes the importance of rigorously validating LLMs as a health information source.

Indexed as

Herpes ZosterHerpes Zoster VaccineMusculoskeletal DiseasesRheumatic DiseasesVaccinationCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMaleReproducibility of ResultsUnited StatesHerpes Zoster VaccineChatGPTlarge language modelsrecombinant zoster vaccinerheumatic and musculoskeletal diseases

Identifiers

PMID39814338
PMCPMC12251431

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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.