Evidence map›Paper›PMID 41536543›Full record

ArticleDigital health

Readability, reliability, and quality of kyphosis-related information provided by artificial intelligence chatbots: A cross-sectional study.

Anil Agar, Sefa Key

Abstract read
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Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

2 authors.

Anil AgarDepartment of Orthopedics, Firat University Hospital, Firat University, Elazig, Turkey.ORCID https://orcid.org/0000-0003-2344-7801
Sefa KeyDepartment of Orthopedics, Firat University Hospital, Firat University, Elazig, Turkey.ORCID https://orcid.org/0000-0003-3620-936X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) chatbots are increasingly used for health information dissemination. However, their effectiveness depends on the clarity, reliability, and quality of the content they deliver. This cross-sectional study aimed to evaluate the readability and reliability of kyphosis-related information provided by six major AI chatbots: ChatGPT, Gemini, Copilot, Perplexity, DeepSeek, and Grok. Methods: We selected the top 10 kyphosis-related questions from Google's "People also ask" section and submitted them to each chatbot. Readability was assessed using FKGL, FKRS, GFOG, SMOG, CL, ARI, and LW indices. Quality and reliability were evaluated using the DISCERN tool, JAMA benchmark, Global Quality Score (GQS), Ensuring Quality Information for Patients (EQIP), and a kyphosis-specific content score (KSC). Statistical analyses were performed using the Kruskal-Wallis and Mann-Whitney U tests. Results: No statistically significant difference was found among chatbots in FKGL, FKRS, SMOG, ARI, or GFOG scores. However, Perplexity had significantly higher DISCERN and EQIP scores, indicating superior content quality. All chatbots presented content at a readability level higher than the AMA-recommended sixth-grade level. While AI models provided more comprehensive and up-to-date information than traditional web sources, their outputs remained challenging for the average patient to comprehend. Conclusions: AI chatbots offer promising tools for disseminating health information about kyphosis but require significant improvements in readability. Expert-reviewed and patient-centered refinements are necessary to ensure accessibility and safety in digital health communication.

Indexed as

artificial intelligenceinformationKyphosispatient educationreadability

Identifiers

PMID41536543
PMCPMC12796146

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.