Evidence map›Paper›PMID 42032531›Full record

ArticleBMC medical education2026

AI-enabled proactive health education: perceptions of public health students - a qualitative study.

Jianyu Wang, Haiyang Lang, Juan Guo, Lin Wu, Junye Liu, Jing Li, Ji Lei, Wenxi Du, Liang Zhu

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Jianyu Wang *Department of Radiation Medical Protection, School of Preventive Medicine, The Fourth Military Medical University, Xi'an, 710032, China.
Haiyang Lang *Department of Radiation Medical Protection, School of Preventive Medicine, The Fourth Military Medical University, Xi'an, 710032, China.
Juan GuoDepartment of Radiation Medical Protection, School of Preventive Medicine, The Fourth Military Medical University, Xi'an, 710032, China.
Lin WuDepartment of Military Medical Psychology, The Fourth Military Medical University, Xi'an, 710032, China.
Junye LiuDepartment of Radiation Medical Protection, School of Preventive Medicine, The Fourth Military Medical University, Xi'an, 710032, China.
Jing LiDepartment of Radiation Medical Protection, School of Preventive Medicine, The Fourth Military Medical University, Xi'an, 710032, China.
Ji LeiDepartment of Health Services Management and Medical Education, School of Preventive Medicine, The Fourth Military Medical University, Xi'an, 710032, China.
Wenxi DuDepartment of Health Services Management and Medical Education, School of Preventive Medicine, The Fourth Military Medical University, Xi'an, 710032, China. 115161763@qq.com.
Liang ZhuDepartment of Health Services Management and Medical Education, School of Preventive Medicine, The Fourth Military Medical University, Xi'an, 710032, China. Liangjulia0317@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aging of global populations and the increasing burden of non-communicable diseases have underscored the critical importance of Proactive Health strategies. Concurrently, artificial intelligence (AI) is transforming the landscape of medical education. Despite these parallel developments, the perspectives of students in public health and preventive medicine regarding AI-enabled Proactive Health education remain largely unexplored, representing a significant gap in the literature.

methodsA descriptive qualitative study was conducted using semi-structured, in-depth interviews with 22 students enrolled in public health and preventive medicine programs. Participants were selected via purposive sampling, and the sample size was determined based on the principle of information saturation. Thematic analysis was employed to analyze the interview data.

resultsAnalysis of the data revealed three primary themes: ambiguous conceptual understanding of Proactive Health among students, a stance of cautious optimism toward the integration of AI in Proactive Health education, a perceived misalignment between current medical education curricula and the competencies required for future practice.

conclusionsProactive Health represents a vital direction for the evolution of public health. However, its effective integration into educational frameworks is currently impeded by conceptual vagueness and disciplinary ambiguities. AI holds substantial potential to empower and innovate Proactive Health education. There is an urgent need for curricular reform to equip future public health practitioners with the necessary core competencies. This study offers practical insights to inform the ongoing reform of education in public health and preventive medicine.

Indexed as

Artificial IntelligenceHealth EducationPreventive MedicinePublic HealthStudents, Public HealthAdultCurriculumFemaleHumansInterviews as TopicMaleQualitative ResearchYoung AdultArtificial intelligenceEducational reformProactive healthPublic health educationQualitative study

Identifiers

PMID42032531
PMCPMC13248282

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.