Evidence map›Paper›PMID 39920711›Full record

ArticleBMC medical education2025

Using ChatGPT for medical education: the technical perspective.

Kwan Yin Chan, Tsz Hon Yuen, Michael Co

Abstract read
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

3 authors.

Kwan Yin ChanDepartment of Computer Science, University of Hong Kong, Pokfulam, Hong Kong. kychan@cs.hku.hk.
Tsz Hon YuenDepartment of Software Systems & Cybersecurity, Monash University, Clayton, Australia.
Michael CoCentre for Education and Training, Department of Surgery, Queen Mary Hospital, University of Hong Kong, Pokfulam, Hong Kong.

Funding

Research Grant Council of Hong Kong 106220117
6 · The paper itself

Abstract

backgroundThe chatbot application Bennie and the Chats was introduced due to the outbreak of COVID-19, which is aimed to provide substitution for teaching conventional clinical history-taking skills. It was implemented with DialogFlow with preset responses, which consists of a large constraint on responding to different conversations. The rapid advancement of artificial intelligence, such as the recent introduction of ChatGPT, offers innovative conversational experiences with computer-generated responses. It provides an idea to develop the second generation of Bennie and the Chats. As the epidemic slows, it can become an assisting tool for students as additional exercise. In this work, we present the second generation of Bennie and the Chats with ChatGPT, which provides room for flexible and expandable improvement.

methodsThe objective of this research is to examine the influence of the newly proposed chatbot on learning efficacy and experiences in bedside teaching, and its potential contributions to international teaching collaboration. This study employs a mixed-method design that incorporates both quantitative and qualitative approaches. From the quantitative approach, we launched the world's first cross-territory virtual bedside teaching with our proposed application and conducted a survey between the University of Hong Kong (HKU) and the National University of Singapore (NUS). Descriptive statistics and Spearman's Correlation were applied for data analysis. From the qualitative approach, a comparative analysis was conducted between the two versions of the chatbot. And, we discuss the interrelationship between the quantitative and qualitative results.

resultsFor the quantitative result, we collected a questionnaire from 45 students about the evaluation of virtual bedside teaching between territories. Over 75% of the students agreed that teaching can enhance learning effectiveness and experience. Moreover, by exchanging patients cases, 82.2% of students agreed that it helps to gain more experiences with diseases that may not be prevalent in their own locality. For the qualitative result, the new chatbot provides better usability and flexibility.

conclusionVirtual bedside teaching with chatbots has revolutionized conventional bedside teaching by its advantages and allowing international collaborations. We believe that the training of history taking skills by chatbot will be a feasible supplementary teaching tool to conventional bedside teaching.

Indexed as

Artificial IntelligenceComputer-Assisted InstructionCOVID-19Education, MedicalGenerative Artificial IntelligenceHong KongHumansSARS-CoV-2SingaporeChatGPTClinical educationMobile learningSimulations

Identifiers

PMID39920711
PMCPMC11806775

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.