ArticleBMC medical education2024
Application of artificial intelligence-assisted image diagnosis software based on volume data reconstruction technique in medical imaging practice teaching.
Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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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.
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.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evaluating AI-powered instruction on Computed Tomography Angiography (CTA) teaching outcomes: a systematic review and meta-analysis.BMC medical education · 2026Pooled it
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- The effectiveness of GAI-assisted teaching methods in medical education: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- Rebuilding ability acquisition in medical imaging teaching: a multi-level AI-driven teaching paradigm based on cognitive-information theory.BMC medical education · 2026Trial
- The Impact of AI on Learners' Self-Efficacy: A Meta-Analysis.Behavioral sciences (Basel, Switzerland) · 2026Review
- Enhancing Medical Education: The Role of Artificial Intelligence Tools in the Teaching-Learning Process.Advances in medical education and practice · 2026Review
- Recent advances in artificial intelligence-assisted medical imaging education.Frontiers in medicine · 2026Review
- Artificial intelligence in medical education for pulmonary nodule management: a narrative review.Translational lung cancer research · 2025Review
- Utilization of artificial intelligence-based pulmonary nodule target reconstruction software in clinical practice education for standardized training residents.BMC medical education · 2025Article
- Review
- Enhancing medical imaging education: integrating computing technologies, digital image processing and artificial intelligence.Journal of medical radiation sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundIn medical imaging courses, due to the complexity of anatomical relationships, limited number of practical course hours and instructors, how to improve the teaching quality of practical skills and self-directed learning ability has always been a challenge for higher medical education. Artificial intelligence-assisted diagnostic (AISD) software based on volume data reconstruction (VDR) technique is gradually entering radiology. It converts two-dimensional images into three-dimensional images, and AI can assist in image diagnosis. However, the application of artificial intelligence in medical education is still in its early stages. The purpose of this study is to explore the application value of AISD software based on VDR technique in medical imaging practical teaching, and to provide a basis for improving medical imaging practical teaching.
methodsTotally 41 students majoring in clinical medicine in 2017 were enrolled as the experiment group. AISD software based on VDR was used in practical teaching of medical imaging to display 3D images and mark lesions with AISD. Then annotations were provided and diagnostic suggestions were given. Also 43 students majoring in clinical medicine from 2016 were chosen as the control group, who were taught with the conventional film and multimedia teaching methods. The exam results and evaluation scales were compared statistically between groups.
resultsThe total skill scores of the test group were significantly higher compared with the control group (84.51 ± 3.81 vs. 80.67 ± 5.43). The scores of computed tomography (CT) diagnosis (49.93 ± 3.59 vs. 46.60 ± 4.89) and magnetic resonance (MR) diagnosis (17.41 ± 1.00 vs. 16.93 ± 1.14) of the experiment group were both significantly higher. The scores of academic self-efficacy (82.17 ± 4.67) and self-directed learning ability (235.56 ± 13.50) of the group were significantly higher compared with the control group (78.93 ± 6.29, 226.35 ± 13.90).
conclusionsApplying AISD software based on VDR to medical imaging practice teaching can enable students to timely obtain AI annotated lesion information and 3D images, which may help improve their image reading skills and enhance their academic self-efficacy and self-directed learning abilities.
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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.