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Emerging Medical Imaging Technologies and Educational Approaches.

2026·0 Zitationen·PubMed
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2

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2026

Jahr

Abstract

PURPOSE: To examine current literature on integrating emerging technologies, artificial intelligence (AI), and informatics into medical imaging education. METHODS: A systematic review of peer-reviewed literature published in the past 5 years was conducted, focusing on medical imaging education, radiography curricula, AI applications, and ethical considerations. Articles were analyzed to identify recurring themes and trends in implementing AI and informatics in medical imaging education programs. RESULTS: Four key themes emerged from the literature: integration of emerging technologies and AI in medical imaging education; foundational informatics concepts and emerging technologies essential for medical imaging professionals; clinical applications of AI in medical imaging practice; and ethical and professional considerations regarding AI adoption. DISCUSSION: Integrating AI and informatics into medical imaging education is increasingly recognized as essential, but curriculum constraints, faculty preparedness, and the evolving nature of AI technologies are challenges to integration. Ethical concerns, including bias in AI algorithms and the potential effect on professional decision-making, highlight the need for responsible implementation. International efforts to establish AI educational frameworks are emerging that emphasize the importance of scaffolding learning to gradually build competency. CONCLUSION: To ensure the safe and effective use of AI in medical imaging, structured education and professional training must be prioritized. Future research should explore best practices for AI and informatics curriculum development, standardized assessment of AI literacy, and long-term effects of AI on clinical decision-making. By addressing these areas, medical imaging professionals can remain at the forefront of technological advancements while maintaining ethical responsibility and patient-centered care.

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Artificial Intelligence in Healthcare and EducationRadiology practices and educationRadiomics and Machine Learning in Medical Imaging
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