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How does the concept of “model as a dataset” reshape traditional data-sharing practices in medical imaging?
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3. It enables sharing of learned model weights instead of sensitive raw images. |
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it facilitates more, giving you more advance generative system |
said in page 1, introductuion and in page 2, use cases in medical imaging |
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| 2 |
Which analytical conclusion can be drawn about the trade-offs between physics-informed and statistical models?
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2. Physics-informed models are more interpretable but computationally intensive. |
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because even though physics-informed models are more domain-specific knowledge, offering high fidlity and interpretabilityo but it require extensive domain expertise and computational resources |
found in page 2 paragraph 3, where they introduce to you 2 types of generative models provide the ability the generate synthetic datasets |
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| 3 |
Why is “mode collapse” considered a critical problem in GAN-based medical image synthesis?
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3. It accelerates model training. |
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It is said that, GANs risk of generating diversity of images |
Found in page 2-3, where they explain to you statistical models |
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| 4 |
Why are healthcare-specific metrics preferred over general-purpose metrics such as FID or SSIM?
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2. They better capture clinical accuracy and diagnostic relevance. |
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| 5 |
What does the article identify as the key tension between privacy preservation and image fidelity?
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2. Privacy protection always lowers model accuracy. |
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even though anonymised data exist, it can still cause risk |
said in page 7, patient privacy and data copying |
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| 6 |
Why is the FDA’s approval of synthetic MRI technology significant for future AI-generated data?
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2. It restricts further AI use in imaging. |
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it's said that FID provides a better estimate of image diversity than inception score, so it's possibly that in the future this can help in accurate information |
found in page 3, evualuating image quality, image metrics |
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| 7 |
Which strategy would best mitigate demographic bias in generative models according to the article?
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1. Increasing sampling from majority populations |
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increasing sampling mean that they will have more diversity variation of not getting potential biases |
found in page 8, potential biases |
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| 8 |
How do DDPMs exemplify versatility in healthcare image synthesis?
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2. They can perform multiple tasks such as denoising, inpainting, and anomaly detection without retraining. |
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found in page 2, paragraph 4 where they tell you about statistical model |
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| 9 |
What analytical insight does the article provide about integrating AI-generated medical images into education and research?
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2. It enhances training by providing diverse, realistic datasets without ethical breaches. |
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| 10 |
Why is regional calibration essential when applying risk prediction models across countries?
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2. To adjust for population-specific incidence and lifestyle differences |
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to be able to predict risk for specifics place/country defending by incidence and lifestyle differences |
found in mostly the last half of the articles, saying that the western prediction can't be compare accurately with east asian because of how diffrent our bodies and lifestyle are |
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| 11 |
What analytical conclusion can be drawn when comparing the China-PAR and Framingham models?
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1. Both overestimate CVD risk in East Asians. |
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found in ASCVD risk prediction in China |
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| 12 |
Based on CVD mortality data, what analytical inference can be made about Japan’s position compared to neighboring countries?
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1. Japan’s low CVD mortality suggests effective prevention and healthcare systems. |
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we can see from figure 1 page 3 that Japan has the lowest mortality rate among the east asian country, and when we look more into the prediction risk in japan we know that their system is quite accurate |
found in page 3 figure 1, and prediction risk in Japan page 9 |
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| 13 |
What analytical limitation arises when using Western-derived coefficients in East Asian models?
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4. It increases sample size artificially. |
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What policy implication can be derived from country-specific risk models?
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1. They allow for targeted national prevention programs. |
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| 15 |
If a model excludes socioeconomic variables, what analytical consequence might occur?
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5. Increased computational efficiency |
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| 16 |
How might AI improve next-generation ASCVD risk prediction in East Asia?
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2. By integrating multimodal data, including imaging and lifestyle information |
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future direction |
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What conclusion can be drawn from comparing Mongolia’s and South Korea’s CVD mortality rates?
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1. Mortality differences reflect varying effectiveness of national prevention programs. |
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| 18 |
What is the most logical future direction for improving ASCVD models across East Asia?
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1. Establishing multinational data-sharing platforms to harmonize regional models |
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so they can have for diversity variation that can be related to themselves accurately |
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| 19 |
According to the “image generation trilemma” shown in the figure, what analytical conclusion can be drawn about the relative strengths of VAEs, GANs, and DDPMs in medical image synthesis?
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3. DDPMs prioritize speed and simplicity over realism. |
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Based on Figure, what analytical conclusion can be drawn regarding the distribution of cardiovascular disease (CVD) subtypes across East Asian countries?
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4. Hemorrhagic stroke accounts for most stroke deaths in Japan, indicating poorer control of blood pressure. |
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