Insights into imaging
August 14, 2026 · via Europe PMC
Assessment of the real MDT teaching model in medical imaging internship: an educational-oriented study.
Yang Q, Xu J, Wang T, An R, Zheng M, Huang Y, Guo F, Ren J.
AI summary — for doctors, generated from the abstract only
A real multidisciplinary team teaching model improved medical imaging interns' practical skills, clinical reasoning and satisfaction versus traditional teaching.
What was studied
Randomized study of 58 imaging interns assigned to real MDT teaching or traditional teaching in a tertiary radiology department.
Key findings
- Practical skills 24.03 vs 23.21; clinical reasoning 25.72 vs 24.17; total 84.41 vs 82.72 (MDT vs control)
- No difference in theoretical knowledge (P=0.197)
- Greater satisfaction with MDT model
Clinical relevance
This concerns medical education without direct patient-care implications.
Who should care
Limitations
Small single-center study; modest score differences.
Learning radiology in real multidisciplinary meetings builds clinical reasoning.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Objectives
To investigate whether the real multidisciplinary team (MDT) teaching mode can improve the learning effectiveness of interns majoring in medical imaging.
Materials and methods
Prospective data from 58 medical imaging interns from July 2023 to January 2024 in a tertiary care radiology department were included. All interns were randomly assigned to either the experimental group or the control group using a random number table, with 29 interns in each group. The control group received the traditional teaching mode, whereas the experimental group was taught using a real MDT teaching model. Following the internship, the theoretical knowledge, practical reading skills, and clinical reasoning and decision test scores of the two groups were compared. Additionally, the teaching effectiveness and satisfaction of the two teaching modes were evaluated via questionnaires.
Results
The practical skills (24.03 ± 1.18), clinical reasoning and decision (25.72 ± 1.85), and total score (84.41 ± 2.77) of the experimental group were significantly higher than those of the control group (23.21 ± 1.37), (24.17 ± 1.14), and (82.72 ± 2.45), respectively. However, there was no statistically significant difference in theoretical knowledge score between the two groups (34.66 ± 2.44 vs 35.34 ± 1.45, p = 0.197). The questionnaire survey results indicated that the experimental group had a more favorable evaluation of the real MDT teaching mode compared to the control group (p 2 = 12.301, p = 0.002).
Conclusions
The real MDT teaching model improves interns' clinical competencies, self-learning, teamwork, and overall educational satisfaction, showing promising utility in medical imaging training.
Key points
Question The traditional teaching mode of radiology internship lacks interdisciplinary integration, which limits the clinical reasoning ability of interns. Findings Compared with the traditional teaching mode, the real MDT teaching mode in this study significantly improved the practical skills, clinical reasoning ability, and satisfaction of interns. Critical relevance statement The new interdisciplinary teaching model significantly improves the teaching effectiveness of radiology interns and helps cultivate their reasoning ability to combine imaging findings with clinical practice. This model is expected to be embedded in the intern training system.
DOI
10.1186/s13244-026-02364-8
PMID
42601532
PMCID
PMC13476203
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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