Evidence map›Paper›PMID 37788057›Full record

SynthesisJournal of medical Internet research2023

Conversational AI and Vaccine Communication: Systematic Review of the Evidence.

Aly Passanante, Ed Pertwee, Leesa Lin, Kristi Yoonsup Lee, Joseph T Wu, Heidi J Larson

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 3 pooled it
7.4field-weighted citation impact, top 2% of its field
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

20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 27 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Chatbots That Deliver Contraceptive Support: Systematic Review.Journal of medical Internet research · 2024
    Pooled it
  4. Trial
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. [Digital innovations in vaccination communication].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025
    Article
  14. Article
  15. Review
  16. Review
  17. Review
  18. [Vaccine hesitancy in postpandemic times-A guide for physicians].Innere Medizin (Heidelberg, Germany) · 2024
    Review
  19. Review
  20. Review
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

6 authors at 3 institutions in 3 countries.

Aly Passanante *Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0002-8931-9549
Ed Pertwee *Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0002-9089-1649
Leesa LinDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0003-4123-4762
Kristi Yoonsup LeeLaboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong, China (Hong Kong).ORCID 0000-0001-7567-0031
Joseph T WuLaboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong, China (Hong Kong).ORCID 0000-0002-3155-5987
Heidi J LarsonDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID 0000-0002-8477-7583
London School of Hygiene & Tropical Medicine · GBUniversity of Hong Kong · HKUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince the mid-2010s, use of conversational artificial intelligence (AI; chatbots) in health care has expanded significantly, especially in the context of increased burdens on health systems and restrictions on in-person consultations with health care providers during the COVID-19 pandemic. One emerging use for conversational AI is to capture evolving questions and communicate information about vaccines and vaccination.

objectiveThe objective of this systematic review was to examine documented uses and evidence on the effectiveness of conversational AI for vaccine communication.

methodsThis systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, Web of Science, PsycINFO, MEDLINE, Scopus, CINAHL Complete, Cochrane Library, Embase, Epistemonikos, Global Health, Global Index Medicus, Academic Search Complete, and the University of London library database were searched for papers on the use of conversational AI for vaccine communication. The inclusion criteria were studies that included (1) documented instances of conversational AI being used for the purpose of vaccine communication and (2) evaluation data on the impact and effectiveness of the intervention.

resultsAfter duplicates were removed, the review identified 496 unique records, which were then screened by title and abstract, of which 38 were identified for full-text review. Seven fit the inclusion criteria and were assessed and summarized in the findings of this review. Overall, vaccine chatbots deployed to date have been relatively simple in their design and have mainly been used to provide factual information to users in response to their questions about vaccines. Additionally, chatbots have been used for vaccination scheduling, appointment reminders, debunking misinformation, and, in some cases, for vaccine counseling and persuasion. Available evidence suggests that chatbots can have a positive effect on vaccine attitudes; however, studies were typically exploratory in nature, and some lacked a control group or had very small sample sizes.

conclusionsThe review found evidence of potential benefits from conversational AI for vaccine communication. Factors that may contribute to the effectiveness of vaccine chatbots include their ability to provide credible and personalized information in real time, the familiarity and accessibility of the chatbot platform, and the extent to which interactions with the chatbot feel "natural" to users. However, evaluations have focused on the short-term, direct effects of chatbots on their users. The potential longer-term and societal impacts of conversational AI have yet to be analyzed. In addition, existing studies do not adequately address how ethics apply in the field of conversational AI around vaccines. In a context where further digitalization of vaccine communication can be anticipated, additional high-quality research will be required across all these areas.

Indexed as

COVID-19VaccinesArtificial IntelligenceCommunicationHumansPandemicsVaccinesartificial intelligencechatbotsconversational agentconversational AICOVID-19health informationvaccine communicationvaccine hesitancyvaccine information

Identifiers

PMID37788057
PMCPMC10582806
OpenAlexW4385437651

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.