Evidence map›Paper›PMID 37910155›Full record

ArticleJMIR medical education2023

Exploring the Possible Use of AI Chatbots in Public Health Education: Feasibility Study.

Francesco Baglivo, Luigi De Angelis, Virginia Casigliani, Guglielmo Arzilli, Gaetano Pierpaolo Privitera, Caterina Rizzo

Open access · goldAbstract read
In one paragraph

Article in JMIR medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 8% 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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.

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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 1 institution in 1 country.

Francesco Baglivo *Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa (PI), Italy.ORCID https://orcid.org/0000-0001-9391-7966
Luigi De Angelis *Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa (PI), Italy.ORCID https://orcid.org/0009-0005-6136-6596
Virginia CasiglianiDepartment of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa (PI), Italy.ORCID https://orcid.org/0000-0002-4184-2648
Guglielmo ArzilliDepartment of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa (PI), Italy.ORCID https://orcid.org/0000-0003-1258-1650
Gaetano Pierpaolo PriviteraDepartment of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa (PI), Italy.ORCID https://orcid.org/0000-0002-8638-2462
Caterina RizzoDepartment of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa (PI), Italy.ORCID https://orcid.org/0000-0002-5583-7508
University of Pisa · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is a rapidly developing field with the potential to transform various aspects of health care and public health, including medical training. During the "Hygiene and Public Health" course for fifth-year medical students, a practical training session was conducted on vaccination using AI chatbots as an educational supportive tool. Before receiving specific training on vaccination, the students were given a web-based test extracted from the Italian National Medical Residency Test. After completing the test, a critical correction of each question was performed assisted by AI chatbots.

objectiveThe main aim of this study was to identify whether AI chatbots can be considered educational support tools for training in public health. The secondary objective was to assess the performance of different AI chatbots on complex multiple-choice medical questions in the Italian language.

methodsA test composed of 15 multiple-choice questions on vaccination was extracted from the Italian National Medical Residency Test using targeted keywords and administered to medical students via Google Forms and to different AI chatbot models (Bing Chat, ChatGPT, Chatsonic, Google Bard, and YouChat). The correction of the test was conducted in the classroom, focusing on the critical evaluation of the explanations provided by the chatbot. A Mann-Whitney U test was conducted to compare the performances of medical students and AI chatbots. Student feedback was collected anonymously at the end of the training experience.

resultsIn total, 36 medical students and 5 AI chatbot models completed the test. The students achieved an average score of 8.22 (SD 2.65) out of 15, while the AI chatbots scored an average of 12.22 (SD 2.77). The results indicated a statistically significant difference in performance between the 2 groups (U=49.5, P<.001), with a large effect size (r=0.69). When divided by question type (direct, scenario-based, and negative), significant differences were observed in direct (P<.001) and scenario-based (P<.001) questions, but not in negative questions (P=.48). The students reported a high level of satisfaction (7.9/10) with the educational experience, expressing a strong desire to repeat the experience (7.6/10).

conclusionsThis study demonstrated the efficacy of AI chatbots in answering complex medical questions related to vaccination and providing valuable educational support. Their performance significantly surpassed that of medical students in direct and scenario-based questions. The responsible and critical use of AI chatbots can enhance medical education, making it an essential aspect to integrate into the educational system.

Indexed as

AI chatbotartificial intelligencechatbot modelchatbotsChatGPTeducational support toolgenerative AIGoogle Bardhealth carehealth educationlarge language modelmedical educationmedical studentsmedical trainingpublic healthvaccination

Identifiers

PMID37910155
PMCPMC10652189
OpenAlexW4387108520

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.