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Applications of generative AI improve diagnostic accuracy in radiology, highlighting trust and adoption challenges.
Foundation models can streamline imaging workflows, improve diagnostic accuracy, enable personalized treatment, and, via integrated generative artificial intelligence, create synthetic images to augment scarce real-world data; however, successful adoption requires explainability, trust, and workflow integration.
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Tavakoli et al. (2025) studied this question.
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