Evidence map›Paper›PMID 41059377›Full record

ArticleJournal of rural medicine : JRM2025

Medical students' perceptions of professional mission in an AI-driven healthcare future: a text mining analysis of reflective essays in Japan.

Nobuyasu Komasawa, Masanao Yokohira

Abstract read
In one paragraph

Article in Journal of rural medicine : JRM, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Nobuyasu KomasawaCommunity Medicine Education Promotion Office, Faculty of Medicine, Kagawa University, Japan.
Masanao YokohiraDepartment of Medical Education, Faculty of Medicine, Kagawa University, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: As artificial intelligence (AI) technologies advance rapidly, their integration into healthcare is transforming the clinical practice landscape. This study aimed to evaluate how second-year medical students perceive their professional mission in an AI-integrated medical future, through a structured essay task, using text-mining analysis to identify emerging themes and attitudes. Methods: A total of 105 second-year medical students at Kagawa university in Japan completed an essay titled "What is your mission in the AI-driven medical world?". Responses were analyzed using KH Coder for frequency analysis, multidimensional scaling, and co-occurrence network mapping. Participants provided verbal informed consent and student anonymity was ensured. Results: The most frequently used terms were medical, consider, think, doctor, AI, human, and patient. Three thematic clusters emerged: (1) career design, (2) AI and medicine, and (3) AI and human. Co-occurrence analysis revealed strong associations between "medical" and both "consider" and "patient", while "patient" was linked to both "AI" and "human", indicating thoughtful reflection on technology's impact on patient care. Conclusion: Second-year medical students in Japan demonstrated critical engagement with the concept of mission formation in the context of AI in healthcare. Their essays reflected a balance between optimism for technological advancement and concern for preserving human-centered care. These findings highlight the importance of implementing systematic career education and future-oriented thinking that is aligned with the characteristics of Generation Z learners.

Indexed as

artificial intelligencecareer designmedical educationmedical professionalismtext mining

Identifiers

PMID41059377
PMCPMC12497985

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.