Evidence map›Paper›PMID 41568112›Full record

ArticleJournal of rural medicine : JRM2026

Comparison of AI chatbot-generated and web-based health information on the effects of radiation in fetuses and children.

Shinya Ito, Emi Furukawa, Tsuyoshi Okuhara, Hiroko Okada, Takahiro Kiuchi

Abstract read
In one paragraph

Article in Journal of rural medicine : JRM, 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

5 authors.

Shinya ItoSchool of Nursing, Kitasato University, Japan.
Emi FurukawaUniversity Hospital Medical Information Network (UMIN) Center, The University of Tokyo Hospital, Japan.
Tsuyoshi OkuharaUniversity Hospital Medical Information Network (UMIN) Center, The University of Tokyo Hospital, Japan.
Hiroko OkadaUniversity Hospital Medical Information Network (UMIN) Center, The University of Tokyo Hospital, Japan.
Takahiro KiuchiUniversity Hospital Medical Information Network (UMIN) Center, The University of Tokyo Hospital, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to assess and compare the readability, understandability, and actionability of radiation-related health information targeting fetuses and children, as provided by Japanese-language web-based sources and AI chatbot-generated content. Furthermore, this study aimed to explore the potential of AI tools to improve access to health information in rural and underserved regions. Materials and Methods: We analyzed 40 publicly accessible Japanese webpages and 30 AI-generated texts produced by ChatGPT (paid and free versions), Copilot, and Gemini. Two prompt types were used: one at the standard reading level and the other at the 6th-grade reading level. Texts were evaluated using the Japanese version of the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P) to assess understandability and actionability, and jReadability to evaluate text complexity. Results: At the standard level, 46.7% of the ChatGPT-4o texts and 78.6% of the Gemini texts achieved PEMAT-P scores ≥70. At the 6th-grade level, all AI-generated texts exceeded this threshold. The AI texts were consistently easier to read than the web-based materials. The paid version of ChatGPT-4o generated slightly more comprehensible text than its free counterpart. However, both AI and web content lack sufficient actionable elements and visual support. Among chatbots, Gemini produced the most user-friendly content, whereas Copilot exhibited notable limitations in terms of coherence and clarity. Conclusion: Even free AI chatbots can generate health information that is easy to read and understand when guided by well-designed prompts. These tools have the potential to reduce health information disparities, especially in rural areas or during disasters where access to professional medical consultations may be limited. Future studies should address the accuracy, reliability, and practical implementation of AI-generated content in real-world health communications.

Indexed as

AI chatbotfetuses and childrenhealth educationradiationreadability

Identifiers

PMID41568112
PMCPMC12816966

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.