| 1 |
What is the primary goal of the article according to its introduction?
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5. To design new diffusion models for image generation |
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The introduction presents the uses of models on various images how it function.
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| 2 |
How do generative AI models differ from traditional discriminative models in healthcare applications?
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2. Generative models produce new data rather than only classify or interpret |
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The generative AI models generate newer information than just mimicking the classic data , we can explore various relationship of different anatomical features
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| 3 |
What is meant by the term “model as a dataset”?
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3. Sharing trained model weights instead of raw data |
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The new concept in data sharing, they store patterns and characteristics from original data in their weights, this allows them to add new synthetic images.
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| 4 |
Which statement correctly distinguishes physics-informed and statistical models?
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3. Physics-informed models incorporate biological or physical principles |
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Physics-informed models are rule based that requires use of mathematical equations and knowledge to generate accurate data whereas statistical models learn from data analysis which a generator creates data samples followed by evaluating these samples
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| 5 |
According to the article, what does the “image generation trilemma” describe?
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2. Trade-offs among image diversity, quality, and speed |
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Artificial intelligence trilemma have various properties when generating images such as capturing all data variations, coverage and speed of sampling.
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| 6 |
What is the Human Turing Test used for in medical image synthesis?
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4. To verify data anonymization |
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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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5. Supporting medical education |
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| 8 |
What is one major ethical concern associated with generative AI in medical imaging?
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2. Data copying and patient reidentification |
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May cause leaking of the patient’s private information
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| 9 |
What regulatory precedent did the article cite for synthetic data technologies?
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2. FDA clearance of synthetic MRI as image-processing software |
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| 10 |
What is the main purpose of the article?
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2. To compare and evaluate ASCVD risk prediction models in East Asia |
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The author wants to define the risks of ASCVD in different regions.Calculate the limitations of current risk and summarize risk stratification.
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| 11 |
Which of the following models was originally developed for a Western population?
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4. Korean Risk Prediction Model (KRPM) |
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| 12 |
Why might Western-based risk prediction models overestimate ASCVD risk in East Asian populations?
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2. East Asians have lower baseline incidence of ASCVD |
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| 13 |
What is the key advantage of the China-PAR model compared to Western-based models?
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4. It was calibrated using national data representing diverse regions in China |
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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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4. Genetic ancestry markers |
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''
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| 15 |
What is a major difference between the Suita Score and the Framingham Risk Score?
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1. Suita Score predicts lifetime risk instead of 10-year risk |
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| 16 |
According to the article, what is a potential benefit of developing East Asia–specific risk models?
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1. They allow prediction of non-cardiovascular diseases |
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| 17 |
Which factor was highlighted as influencing ASCVD risk differences among East Asian countries?
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4. Higher rates of genetic mutations |
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| 18 |
What future direction does the article suggest for improving ASCVD risk prediction?
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2. 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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2. GANs explicitly use noise diffusion steps to reconstruct input data. |
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| 20 |
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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4. China’s lower crude mortality rate compared to its age-standardized rate indicates overestimation of CVD prevalence. |
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