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What is the primary goal of the article according to its introduction?
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To explore advancements, applications, and challenges of generative AI in medical imaging |
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How do generative AI models differ from traditional discriminative models in healthcare applications?
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Generative models produce new data rather than only classify or interpret |
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Generative artificial intelligence is a class of deep learning models capable of creating content that diverges from traditional discriminative models
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What is meant by the term “model as a dataset”?
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Sharing trained model weights instead of raw data |
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| 4 |
Which statement correctly distinguishes physics-informed and statistical models?
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Physics-informed models rely on text prompts |
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According to the article, what does the “image generation trilemma” describe?
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Trade-offs among image diversity, quality, and speed |
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| 6 |
What is the Human Turing Test used for in medical image synthesis?
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To assess realism of synthetic medical images by experts |
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Use cases of synthetic imaging datasets and image generation models in medicine and their findings
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| 7 |
Which of the following is NOT mentioned as a potential benefit of synthetic data in healthcare?
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Facilitating multi-centre collaborations |
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We performed a literature search using the following term combinations: "synthetic data" OR "VAE" OR "GAN∗" OR "diffusion model∗" AND "medical imag∗" OR "radiolog∗" OR "dermatolog∗" OR "patholog∗", and selected a representative paper matching each type of synthetic data for inclusion.
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What is one major ethical concern associated with generative AI in medical imaging?
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Overuse of diffusion models |
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| 9 |
What regulatory precedent did the article cite for synthetic data technologies?
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WHO approval of AI diagnostic models |
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| 10 |
What is the main purpose of the article?
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To compare and evaluate ASCVD risk prediction models in East Asia |
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ASCVD risk in East Asian people living in the United States.
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| 11 |
Which of the following models was originally developed for a Western population?
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Framingham Risk Score |
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Asian immigrantsatherosclerosisChinacoronary artery diseaseEast AsiaJapanKoreastroke
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| 12 |
Why might Western-based risk prediction models overestimate ASCVD risk in East Asian populations?
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East Asians have identical lifestyles to Western populations |
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What is the key advantage of the China-PAR model compared to Western-based models?
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It includes both genetic and lifestyle factors |
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| 14 |
Which of the following variables is not typically included in ASCVD risk prediction models discussed in the article?
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Blood pressure |
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Central Illustration
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What is a major difference between the Suita Score and the Framingham Risk Score?
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Framingham model excludes cholesterol levels |
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According to the article, what is a potential benefit of developing East Asia–specific risk models?
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They improve accuracy and reduce overestimation of risk |
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Which factor was highlighted as influencing ASCVD risk differences among East Asian countries?
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Identical healthcare expenditure |
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What future direction does the article suggest for improving ASCVD risk prediction?
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Using multimodal AI-based prediction integrated with regional data |
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| 19 |
Which statement best explains the key difference in how VAEs, GANs, and DDPMs generate medical images according to the figure?
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DDPMs iteratively remove noise through reverse diffusion rather than using encoder–decoder or discriminator structures. |
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Denoising diffusion probabilistic models (DDPMs) introduce noise into an image and learn to reverse this process, producing high-quality samples.
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Which of the following best explains the trend shown in Figure comparing age-standardized and crude CVD mortality rates among East Asian countries?
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Mongolia and North Korea demonstrate higher CVD mortality due to older population structures alone. |
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จากในกราฟที่โจทย์ให้มา
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