Evidence map›Paper›PMID 42166787›Full record

ArticleJMIR infodemiology2026

Japanese-Language AI Agent System for Human Papillomavirus Vaccine Infoveillance and Public Communication: Development and Feasibility Evaluation.

Junyu Liu, Siwen Yang, Dexiu Ma, Qian Niu, Zequn Zhang, Momoko Nagai-Tanima, Tomoki Aoyama

Abstract read
In one paragraph

Article in JMIR infodemiology, 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.

Junyu LiuGraduate School of Medicine, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan, 81 075-753-7531.ORCID 0000-0001-6232-4199
Siwen YangDavid R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.ORCID 0009-0006-5720-6490
Dexiu MaDepartment of Computer Science, Whitacre College of Engineering, Texas Tech University, Lubbock, TX, United States.ORCID 0009-0003-8221-2639
Qian NiuGraduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.ORCID 0000-0001-7139-384X
Zequn ZhangDepartment of EEIS, University of Science and Technology of China, Hefei, Anhui, China.ORCID 0000-0001-5566-761X
Momoko Nagai-TanimaGraduate School of Medicine, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan, 81 075-753-7531.ORCID 0000-0003-3972-8800
Tomoki AoyamaGraduate School of Medicine, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan, 81 075-753-7531.ORCID 0009-0002-5172-5477

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Human papillomavirus (HPV) vaccine hesitancy remains a significant public health challenge in Japan, where proactive vaccination recommendations were suspended between 2013 and 2021. The resulting information gap between medical institutions and vaccine-hesitant populations is exacerbated by misinformation on social media platforms. Traditional public health communication strategies cannot address individual queries while simultaneously monitoring population-level discourse. Objective: This study aimed to develop and conduct a feasibility evaluation of a dual-purpose artificial intelligence agent system that delivers verified HPV vaccine information to the public through a conversational interface while generating infoveillance reports for medical institutions based on user interactions and social media discourse. Methods: We implemented a system with 3 components: a vector database integrating 139,803 documents, including academic papers, Japanese government sources, news media, and social media posts; a retrieval-augmented generation chatbot using a ReAct agent architecture with iterative multitool orchestration across 5 specialized knowledge sources; and an automated report generation system with modules for news analysis, research synthesis, social media sentiment analysis, including stance classification and topic modeling, and user interaction pattern identification. System performance was assessed using both automated and manual evaluation protocols on a scale from 0 to 5. Results: The entire system functioned as expected. For single-turn evaluation, the chatbot achieved mean scores of 4.83 (SD 0.67; 95% CI 4.71-4.93) for relevance, 4.89 (SD 0.53; 95% CI 4.79-4.97) for routing, 4.50 (SD 1.29; 95% CI 4.27-4.70) for reference quality, 4.90 (SD 0.62; 95% CI 4.78-4.99) for correctness, and 4.88 (SD 0.54; 95% CI 4.78-4.96) for professional identity, with an overall mean of 4.80. Multiturn evaluation yielded higher mean scores: 4.94 for context memory (SD 0.25; 95% CI 4.84-5.00) and an overall mean of 4.98, with topic centering and identity achieving 5.00. The report generation system achieved high scores across all sections: 4.83 for completeness (SD 0.37; 95% CI 4.73-4.94), 4.88 for correctness (SD 0.33; 95% CI 4.77-4.96), and 4.12 for helpfulness (SD 0.48; 95% CI 3.98-4.27). Reference validity achieved perfect scores (5.00) across all periods, with citation correctness averaging 4.21 (SD 0.58; 95% CI 3.96-4.46). Conclusions: This feasibility study demonstrated that an integrated artificial intelligence agent system can support both public HPV vaccine communication and social media infoveillance in a Japanese-language context. Prospective deployment with real users is needed to assess actual public health impact.

Indexed as

Artificial IntelligenceHealth CommunicationPapillomavirus VaccinesCommunicationFeasibility StudiesHuman Papillomavirus VirusesHumansJapanLanguagePapillomavirus InfectionsSocial MediaPapillomavirus VaccinesAIAI agentartificial intelligenceartificial intelligence agentHPVhuman papillomaviruslarge language modelstance analysistopic modeling

Identifiers

PMID42166787
PMCPMC13193703

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.