Evidence map›Paper›PMID 42258213›Full record

Trial reportJAMA network open2026

Large Language Model Chatbot Conversations vs Public Health Materials and Parental HPV Vaccination Intentions: A Randomized Clinical Trial.

Neil K R Sehgal, Sunny Rai, Manuel Tonneau, Anish K Agarwal, Joseph Cappella, Melanie L Kornides, Lyle Ungar, Alison Buttenheim, Sharath Chandra Guntuku

Registry-linked trialAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JAMA network open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07132125 (Conversations With AI Chatbots Increase Short-Term Vaccine Intentions But Do Not Outperform Standard Public Health Messaging), which is not on this 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.

NCT07132125 nacompletednot on this map

Conversations With AI Chatbots Increase Short-Term Vaccine Intentions But Do Not Outperform Standard Public Health Messaging

TypeinterventionalSponsorUniversity of PennsylvaniaRan2025 to 2025Enrolled930ConditionsHPV Vaccination IntentArmsDefault-style LLM chatbot, Conversational-style LLM chatbot, Public health informational materials
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

9 authors.

Neil K R SehgalComputer and Information Science Department, University of Pennsylvania, Philadelphia.
Sunny RaiComputer and Information Science Department, University of Pennsylvania, Philadelphia.
Manuel TonneauOxford Internet Institute, University of Oxford, Oxford, UK.
Anish K AgarwalLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Joseph CappellaLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Melanie L KornidesLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Lyle UngarComputer and Information Science Department, University of Pennsylvania, Philadelphia.
Alison ButtenheimLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Sharath Chandra GuntukuComputer and Information Science Department, University of Pennsylvania, Philadelphia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Health care systems are increasingly considering large language model (LLM)-based chatbots for vaccine communication, but evidence that they improve durable, behaviorally relevant outcomes beyond existing health materials is limited. Objectives: To examine whether brief, multiturn interactions with an LLM chatbot increase parental intention to vaccinate children against human papillomavirus (HPV) compared with no intervention and government public health materials and to assess whether any effects persist. Design, Setting, and Participants: This randomized clinical trial was conducted online among individuals in the US, Canada, and the UK from March 3 to May 25, 2025, with follow-up at 15 and 45 days. Eligible participants were parents 18 years or older with at least 1 HPV vaccine-eligible child (aged 11-17 years in the US and Canada and 12-17 years in the UK) who had not received HPV vaccination or whose vaccination status was unknown. Interventions: Participants were randomized to (1) no-message control, (2) government public health materials matched to country (minimum 3-minute exposure), or (3) a 3-minute interaction with an LLM chatbot (OpenAI's GPT-4o) prompted to encourage HPV vaccination using a default response style or a shorter conversational style. Main Outcomes and Measures: The primary outcome was self-reported likelihood of vaccinating the child against HPV within 12 months (0- to 100-point scale, with 0 indicating extremely unlikely and 100 indicating extremely likely) measured immediately after intervention. Prespecified follow-ups included vaccination intent and self-reported vaccination at 15 and 45 days. Results: A total of 1297 participants (mean [SD] age, 42.84 [6.93] years; 935 [72.1%] female) were randomized. Compared with no intervention, public health materials increased immediate vaccination intent (Cohen d = 0.53; 95% CI, 0.36-0.70), as did the default chatbot (d = 0.48; 95% CI, 0.30-0.65) and conversational chatbot (d = 0.33; 95% CI, 0.17-0.49). At 45 days, neither chatbot increased intent relative to controls, whereas public health materials maintained modest effects. No intervention increased self-reported vaccination uptake. Conclusions and Relevance: The findings of this randomized clinical trial suggest that well-designed public health messaging may match or exceed the impact of short chatbot conversations for HPV vaccine promotion. Trial Registration: ClinicalTrials.gov Identifier: NCT07132125.

Indexed as

Papillomavirus InfectionsPapillomavirus VaccinesParentsVaccinationAdolescentAdultCanadaChildFemaleHumansIntentionLarge Language ModelsMaleMiddle AgedPublic HealthUnited KingdomPapillomavirus Vaccines

Identifiers

PMID42258213
PMCPMC13247808

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Registered trials

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.