Evidence map›Paper›PMID 37428336›Full record

ArticleAnnals of biomedical engineering2024

ChatGPT and Vaccines: Can AI Chatbots Boost Awareness and Uptake?

Shahab Saquib Sohail, Dag Øivind Madsen, Faiza Farhat, M Afshar Alam

Abstract readLetter
PubMed Publisher
In one paragraph

Article in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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

4 authors.

Shahab Saquib SohailDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, 110062, India.
Dag Øivind MadsenUSN School of Business, University of South-Eastern Norway, 3511, Hønefoss, Norway. dag.oivind.madsen@usn.no.ORCID http://orcid.org/0000-0001-8735-3332
Faiza FarhatDepartment of Zoology, Aligarh Muslim University, Aligarh, U.P., 202002, India.
M Afshar AlamDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, 110062, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global COVID-19 pandemic has affected all spheres of human life, resulting in millions of deaths and overwhelming medical facilities. Moreover, the world has witnessed great financial hardship because of job losses resulting in economic havoc. Many sections of society have contributed in different ways to slow the spread of the virus and protect public health. For example, medical scientists are praised for their efforts to develop COVID-19 vaccines. Clinical trials have shown that the COVID-19 vaccines are highly effective in preventing symptomatic COVID-19 infections. However, many people around the world have been hesitant to get vaccinated. Vaccine misconceptions have emerged and increased due to a combination of factors, including the availability of information on the Internet and the influence of celebrities and opinion leaders. In this context, we have analyzed ChatGPT responses to relevant queries on vaccine misconceptions. The positive responses and supportive opinions provided by the AI chatbot could be instrumental in shaping people's perceptions of vaccines and in encouraging users to get vaccinated and reduce misconceptions.

Indexed as

COVID-19VaccinesBiological TransportCOVID-19 VaccinesHumansPandemicsCOVID-19 VaccinesVaccinesArtificial intelligenceChatGPTCOVID-19VaccineVaccine hesitancy

Identifiers

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.