Evidence map›Paper›PMID 42643509›Full record

ArticleDigital health

Artificial intelligence chatbots as information sources for postherpetic neuralgia: An evaluation of reliability and readability.

Chenxin Sun, Jingxuan Sun, Shihao Jing, Yong Fei, Ling Ma, Jianjun Zhu

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chenxin SunDepartment of Pain Medicine, The First Hospital of Jiaxing, Jiaxing, Zhejiang, China.
Jingxuan SunDepartment of Pain Medicine, The First Hospital of Jiaxing, Jiaxing, Zhejiang, China.ORCID https://orcid.org/0009-0005-8269-2227
Shihao JingThe First Clinical Medical College, Anhui Medical University, Hefei, Anhui, China.
Yong FeiDepartment of Pain Medicine, The First Hospital of Jiaxing, Jiaxing, Zhejiang, China.
Ling MaDepartment of Pain Medicine, The First Hospital of Jiaxing, Jiaxing, Zhejiang, China.
Jianjun ZhuDepartment of Pain Medicine, The First Hospital of Jiaxing, Jiaxing, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) chatbots are increasingly used for online health information seeking and have become a common source of information on postherpetic neuralgia (PHN). The quality and readability of the information they provide have not been systematically evaluated. Objective: To assess the quality, transparency, and readability of postherpetic neuralgia information generated by mainstream artificial intelligence chatbots and to examine whether this content meets the recommended sixth-grade reading level for patient education materials. Methods: Using the MeSH term Neuralgia, Postherpetic, we identified 11 highly relevant postherpetic neuralgia-related search queries worldwide from 2020 to 2025 through Google Trends. These queries were entered verbatim into ChatGPT-4o, Gemini-1.5, Perplexity Pro, and Copilot using a standardised input procedure. Responses were assessed with DISCERN, EQIP, JAMA, and GQS for information quality, completeness, transparency, and overall educational value. Readability was evaluated with ARI, FRES, GFI, FKGL, CL, and SMOG. The Kruskal-Wallis test was used for between-model comparisons, and the Wilcoxon signed-rank test was used to compare readability indices against sixth-grade reading benchmarks. Results: DISCERN, EQIP, and GQS scores differed significantly across chatbots in DISCERN (P < 0.001), EQIP (P < 0.001), and GQS ( Conclusion: Mainstream artificial intelligence chatbots can provide postherpetic neuralgia information with moderate quality, but the readability of this content remains consistently above recommended patient education levels. The findings should be interpreted within the scope of the selected chatbots, default operational settings, search queries, and the time-sensitive nature of chatbot outputs. Further improvement in source reporting and plain-language presentation remains warranted.

Indexed as

AI chatbotpostherpetic neuralgiaqualityreadability

Identifiers

PMID42643509
PMCPMC13504130

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.