| 1 |
What is the primary goal of the article according to its introduction?
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3. To explore advancements, applications, and challenges of generative AI in medical imaging |
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To know the progress of what will be developed in the future.
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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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Generative models produce new data rather than only classify or interpret existing information, allowing them to create synthetic medical images or records for research and training.
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patient safety.
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| 3 |
What is meant by the term “model as a dataset”?
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4. A database of patient histories |
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A database of patient histories includes detailed records of each patient’s past illnesses, treatments, lab results, allergies, medications, and medical outcomes.
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1. Privacy & Security
2. Accuracy & Completeness
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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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| 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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| 6 |
What is the Human Turing Test used for in medical image synthesis?
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2. To assess realism of synthetic medical images by experts |
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To evaluate the quality of synthetic images and reduce the risk of real patient data because synthetic images are used instead.
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Realism is a criterion. Privacy and AI training.
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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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4. Eliminating all medical biases permanently |
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It doesn't get rid of permanent medical bias.
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| 8 |
What is one major ethical concern associated with generative AI in medical imaging?
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5. Overuse of diffusion models |
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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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4. To introduce new diagnostic imaging technologies |
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| 11 |
Which of the following models was originally developed for a Western population?
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5. NIPPON Data80 Model |
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| 12 |
Why might Western-based risk prediction models overestimate ASCVD risk in East Asian populations?
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3. Western models use smaller datasets |
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| 13 |
What is the key advantage of the China-PAR model compared to Western-based models?
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1. 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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4. Genetic ancestry markers |
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| 15 |
What is a major difference between the Suita Score and the Framingham Risk Score?
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2. Suita Score was designed for a Japanese population using local epidemiological data |
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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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5. They can replace medical examinations entirely |
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| 17 |
Which factor was highlighted as influencing ASCVD risk differences among East Asian countries?
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3. Use of identical clinical guidelines |
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| 18 |
What future direction does the article suggest for improving ASCVD risk prediction?
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4. Ignoring socioeconomic determinants |
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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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3. DDPMs iteratively remove noise through reverse diffusion rather than using encoder–decoder or discriminator structures. |
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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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3. Despite differences in age structures, Japan maintains low mortality rates in both measures, suggesting effective prevention and healthcare systems. |
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