| 1 |
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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Seems the most logical, generative AI still requires approval from medical experts incase of glitches. |
With the sharing of model weights, the AI can learn more about different types of problems. |
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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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The rest of the choices are false. Choice 2 is the most reasonable. |
Physics- informed model uses theoretical correct mathematical equations and the principle of physics. |
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| 3 |
Why is “mode collapse” considered a critical problem in GAN-based medical image synthesis?
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2. It reduces image realism and variety by producing repetitive outputs. |
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Mode collapse doesn’t fit some of the choices. |
“The GANs excel at generating high-quality samples but might not always capture all data variations, leading to low mode coverage, known as mode collapse.” |
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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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FID and SSIM only evaluate the quality of the generated image. |
Panel 2: Exploring the potential of generative artificial intelligence in medical image synthesis: opportunities, challenges, and future directions. |
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| 5 |
What does the article identify as the key tension between privacy preservation and image fidelity?
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1. Higher realism may risk reproducing identifiable patient data. |
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Generative AI is still under exploration. The risk if data copying on another patient is likely. |
The article stated that one of the biggest challenges on privacy is 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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1. It establishes a framework for validating synthetic data equivalence in clinical use. |
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All the answers wasn’t mentioned in the article. |
The FDA and the European Medicines Agency “will play a crucial role in establishing frameworks for validating and approving synthetic data for clinical applications.” - from the article. |
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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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The other choices are actually supporting the demographic bias in generative models. |
Future research directions: “Establishing multi-institutional collaboratives to create demographically balanced training data.” |
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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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The list of multiple tasks in choice 2. all describes the strengths of DDPMs. |
“DDPMs generate data by learning to reverse a noising process.” “DDPMs can produce high quality samples that closely resemble the training data.” |
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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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Most logical |
Generative AI has large sets of data on basically everything, students can use the database to study deeper into specific topics. |
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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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People live in different regions and have different lifestyles. |
Genetic variation plays a huge role in how people get affected, some are born with genetic disadvantages. |
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| 11 |
What analytical conclusion can be drawn when comparing the China-PAR and Framingham models?
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2. China-PAR uses local epidemiological data, leading to improved predictive validity. |
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From the article, China-PAR is created for the Chinese population. While Framingham is used over a 10 year risk assesment. |
China-PAR includes several factors like ancestral trial and lifestyle. |
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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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Japan’s healthcare system is effective. |
Japan prevention is why the mortality rate is low. |
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| 13 |
What analytical limitation arises when using Western-derived coefficients in East Asian models?
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2. It introduces systematic overestimation of ASCVD probability. |
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| 14 |
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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For ASCVD, you can’t generalize the data. |
The data differed from all countries and regions. |
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| 15 |
If a model excludes socioeconomic variables, what analytical consequence might occur?
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2. Ignored non-biological determinants of disease |
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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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AI is smart and can use all the data ever on the internet to assess. |
AI can possibly create a formula linking to dietary, lifestyles and other factors. |
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| 17 |
What conclusion can be drawn from comparing Mongolia’s and South Korea’s CVD mortality rates?
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2. Both have identical age-adjusted mortality rates. |
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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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| 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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2. GANs provide a balance between image quality and diversity but may suffer from mode collapse. |
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GANs is described perfectly. |
The article shows that GANs is perfect for image enhancing. |
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| 20 |
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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1. Ischemic heart disease (IHD) accounts for a higher proportion of CVD deaths in Japan and South Korea compared with China, suggesting regional lifestyle or prevention differences. |
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