ReviewFrontiers in medicine2026
Recent advances in artificial intelligence-assisted medical imaging education.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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0 citing papers in PubMed.
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Authors and funding
7 authors.
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Abstract
Introduction: The deep integration of artificial intelligence (AI) and medical imaging represents a major trend in the transformation of healthcare, driving advancements in technologies such as image reconstruction. At the same time, medical schools worldwide are integrating AI into medical imaging education. This paper reviews recent advances in artificial intelligent medical imaging education and offers recommendations regarding curriculum design and faculty development for training professionals in artificial intelligent medical imaging. Methods: This study analyzed the application of AI in medical imaging education and corresponding talent development models through a literature review of core databases such as PubMed and Web of Science, supplemented by case studies, to draw conclusions and propose targeted recommendations. Results: AI has been widely applied in medical imaging education to enhance educational quality and other aspects. However, globally, AI-related radiology education exhibits inconsistencies in curriculum design and insufficient integration of technology. Although preliminary evidence suggests that AI can effectively improve teaching outcomes, the lack of standardized teaching guidelines has led to gaps in the knowledge system. Conclusion: The integration of AI and medical imaging offers significant advantages in medical imaging education. However, while the education sector has already adopted various strategies-such as human-machine collaborative education-it still faces challenges, including a shortage of interdisciplinary faculty and a disconnect between the curriculum and clinical practice. Improvements must be made through strategies such as faculty development, pedagogical transformation, fostering AI literacy, and standardizing teaching frameworks. Future research should explore the adaptability of AI across different training stages to promote the sustainable integration of these two fields and the development of relevant professionals.
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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.