Evidence map›Paper›PMID 40082889›Full record

ArticleBMC medical education2025

Integration of artificial intelligence in radiology education: a requirements survey and recommendations from faculty radiologists, residents, and medical students.

Ruili Li, Guangxue Liu, Miao Zhang, Dongdong Rong, Zhuangzhi Su, Yi Shan, Jie Lu

Abstract read
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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 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

7 authors.

Ruili Li *Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Guangxue Liu *Department of Natural Medicines, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, 100191, China.
Miao ZhangDepartment of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Dongdong RongDepartment of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Zhuangzhi SuDepartment of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Yi ShanDepartment of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Jie LuDepartment of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China. imaginglu@hotmail.com.

Funding

Beijing Education Science 13th Five-Year Plan Project CHDB2020174Beijing Residential Training Quality Improvement Project 2023007Beijing Residential Training Quality Improvement Project 2023033Research Project on Education and Teaching Reform of Capital Medical University 2023JYY136
6 · The paper itself

Abstract

backgroundTo investigate the perspectives and expectations of faculty radiologists, residents, and medical students regarding the integration of artificial intelligence (AI) in radiology education, a survey was conducted to collect their opinions and attitudes on implementing AI in radiology education.

methodsAn online questionnaire was used for this survey, and the participant anonymity was ensured. In total, 41 faculty radiologists, 38 residents, and 120 medical students from the authors' institution completed the questionnaire.

resultsMost residents and students experience different levels of psychological stress during the initial stage of clinical practice, and this stress mainly stems from tight schedules, heavy workloads, apprehensions about making mistakes in diagnostic report writing, as well as academic or employment pressures. Although most of the respondents were not familiar with how AI is applied in radiology education, a substantial proportion of them expressed eagerness and enthusiasm for the integration of AI into radiology education. Especially among radiologists and residents, they showed a desire to utilize an AI-driven online platform for practicing radiology skills, including reading medical images and writing diagnostic reports, before engaging in clinical practice. Furthermore, faculty radiologists demonstrated strong enthusiasm for the notion that AI training platforms can enhance training efficiency and boost learners' confidence. Notably, only approximately half of the residents and medical students shared the instructors' optimism, with the remainder expressing neutrality or concern, emphasizing the need for robust AI feedback systems and user-centered designs. Moreover, the authors' team has developed a preliminary framework for an AI-driven radiology education training platform, consisting of four key components: imaging case sets, intelligent interactive learning, self-quiz, and online exam.

conclusionsThe integration of AI technology in radiology education has the potential to revolutionize the field by providing innovative solutions for enhancing competency levels and optimizing learning outcomes.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelFaculty, MedicalRadiologistsRadiologyStudents, MedicalAdultFemaleHumansInternship and ResidencyMaleSurveys and QuestionnairesAI education platformArtificial intelligenceOnline questionnaireRadiology education

Identifiers

PMID40082889
PMCPMC11908051

What OpenQuestion holds

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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.