Evidence map›Paper›PMID 39910559›Full record

ArticleBMC medical education2025

AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation.

Shimpei Okamoto, Masanori Kataoka, Makoto Itano, Tsutomu Sawai

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 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. 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.

Shimpei OkamotoGraduate School of Humanities and Social Sciences, Hiroshima University, Higashi-Hiroshima, Japan.ORCID http://orcid.org/0009-0007-7347-1179
Masanori KataokaUehiro Division for Applied Ethics, Graduate School of Humanities and Social Sciences, Hiroshima University, Higashi-Hiroshima, Japan.ORCID http://orcid.org/0000-0002-8687-0589
Makoto ItanoGraduate School of Humanities and Social Sciences, Hiroshima University, Higashi-Hiroshima, Japan.ORCID http://orcid.org/0009-0004-1627-0803
Tsutomu SawaiGraduate School of Humanities and Social Sciences, Hiroshima University, Higashi-Hiroshima, Japan. tstmsw@hiroshima-u.ac.jp.ORCID http://orcid.org/0000-0002-3806-0573

Funding

Japan Society for the Promotion of Science 21K12908Uehiro Foundation on Ethics and Education UEHIRO2023-0111
6 · The paper itself

Abstract

backgroundWith artificial intelligence (AI) increasingly revolutionising medicine, this study critically evaluates the integration of large language models (LLMs), known for advanced text processing and generation capabilities, in medical ethics education, focusing on promoting virtue. Positing LLMs as central to mimicking nuanced human communication, it examines their use in medical education and the ethicality of embedding AI in such contexts.

methodUsing a hybrid approach that combines principlist and non-principlist methodologies, we position LLMs as exemplars and advisors.

resultsWe discuss the imperative for including AI ethics in medical curricula and its utility as an educational tool, identify the lack of educational resources in medical ethics education, and advocate for future LLMs to mitigate this problem as a "second-best" tool. We also emphasise the critical importance of instilling virtue in medical ethics education and illustrate how LLMs can effectively impart moral knowledge and model virtue cultivation. We address expected counter-arguments to using LLMs in this area and explain their profound potential to enrich medical ethics education, including facilitating the acquisition of moral knowledge and developing ethically grounded practitioners.

conclusionsThe study involved a comprehensive exploration of the function of LLMs in medical ethics education, positing that tools such as ChatGPT can profoundly enhance the learning experience in the future. This is achieved through tailored, interactive educational encounters while addressing the ethical nuances of their use in educational settings.

Indexed as

Artificial IntelligenceEducation, MedicalEthics, MedicalVirtuesCurriculumHumansLarge Language ModelsEducationMedical ethicsMorals

Identifiers

PMID39910559
PMCPMC11796193

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.