Evidence map›Paper›PMID 38032695›Full record

ArticleJMIR medical education2023

Developing Medical Education Curriculum Reform Strategies to Address the Impact of Generative AI: Qualitative Study.

Ikuo Shimizu, Hajime Kasai, Kiyoshi Shikino, Nobuyuki Araki, Zaiya Takahashi, Misaki Onodera, Yasuhiko Kimura, Tomoko Tsukamoto, Kazuyo Yamauchi, Mayumi Asahina and 2 more

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 36 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 2 pooled it
2.1field-weighted citation impact, top 10% 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

36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 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

12 authors at 2 institutions in 1 country.

Ikuo ShimizuDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-6731-7104
Hajime KasaiDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-6759-2026
Kiyoshi ShikinoHealth Professional Development Center, Chiba University Hospital, Chiba, Japan.ORCID https://orcid.org/0000-0002-3721-3443
Nobuyuki ArakiDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-5703-3829
Zaiya TakahashiDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-8520-9502
Misaki OnoderaDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0009-0002-0126-9143
Yasuhiko KimuraHealth Professional Development Center, Chiba University Hospital, Chiba, Japan.ORCID https://orcid.org/0009-0009-6844-7347
Tomoko TsukamotoDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0002-1350-203X
Kazuyo YamauchiHealth Professional Development Center, Chiba University Hospital, Chiba, Japan.ORCID https://orcid.org/0000-0001-6587-2393
Mayumi AsahinaHealth Professional Development Center, Chiba University Hospital, Chiba, Japan.ORCID https://orcid.org/0000-0003-1102-4334
Shoichi ItoDepartment of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0003-3659-6152
Eiryo KawakamiDepartment of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.ORCID https://orcid.org/0000-0001-9955-4342
Chiba University · JPChiba University Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenerative artificial intelligence (GAI), represented by large language models, have the potential to transform health care and medical education. In particular, GAI's impact on higher education has the potential to change students' learning experience as well as faculty's teaching. However, concerns have been raised about ethical consideration and decreased reliability of the existing examinations. Furthermore, in medical education, curriculum reform is required to adapt to the revolutionary changes brought about by the integration of GAI into medical practice and research.

objectiveThis study analyzes the impact of GAI on medical education curricula and explores strategies for adaptation.

methodsThe study was conducted in the context of faculty development at a medical school in Japan. A workshop involving faculty and students was organized, and participants were divided into groups to address two research questions: (1) How does GAI affect undergraduate medical education curricula? and (2) How should medical school curricula be reformed to address the impact of GAI? The strength, weakness, opportunity, and threat (SWOT) framework was used, and cross-SWOT matrix analysis was used to devise strategies. Further, 4 researchers conducted content analysis on the data generated during the workshop discussions.

resultsThe data were collected from 8 groups comprising 55 participants. Further, 5 themes about the impact of GAI on medical education curricula emerged: improvement of teaching and learning, improved access to information, inhibition of existing learning processes, problems in GAI, and changes in physicians' professionality. Positive impacts included enhanced teaching and learning efficiency and improved access to information, whereas negative impacts included concerns about reduced independent thinking and the adaptability of existing assessment methods. Further, GAI was perceived to change the nature of physicians' expertise. Three themes emerged from the cross-SWOT analysis for curriculum reform: (1) learning about GAI, (2) learning with GAI, and (3) learning aside from GAI. Participants recommended incorporating GAI literacy, ethical considerations, and compliance into the curriculum. Learning with GAI involved improving learning efficiency, supporting information gathering and dissemination, and facilitating patient involvement. Learning aside from GAI emphasized maintaining GAI-free learning processes, fostering higher cognitive domains of learning, and introducing more communication exercises.

conclusionsThis study highlights the profound impact of GAI on medical education curricula and provides insights into curriculum reform strategies. Participants recognized the need for GAI literacy, ethical education, and adaptive learning. Further, GAI was recognized as a tool that can enhance efficiency and involve patients in education. The study also suggests that medical education should focus on competencies that GAI hardly replaces, such as clinical experience and communication. Notably, involving both faculty and students in curriculum reform discussions fosters a sense of ownership and ensures broader perspectives are encompassed.

Indexed as

artificial intelligencecurriculum reformgenerative artificial intelligencelarge language modelsmedical educationqualitative analysisstrengths-weaknesses-opportunities-threats (SWOT) framework

Identifiers

PMID38032695
PMCPMC10722362
OpenAlexW4388861785

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.