โจทย์ ปรนัย
What is the primary goal of the article according to its introduction?

Becuase the topic is Exploringthe potential of A in medical imeage synthesis and line 22 the introduction state the article provide of synthetic data in medical imaging and critically examines its advancements, applications, and challenges. Other options are not the main focus.

line 22 start with the start sentence is This viewpoint providw comprehensive overview of.......... , This is the conclude introduction. For MED-Palm , Med Gemini and another website. My teacher teach reading skill me the end of introduction is conclude all detail stories and the topic , When read and find the keyword(viewpoint provide) this is the answer.

โจทย์ ปรนัย
How do generative AI models differ from traditional discriminative models in healthcare applications?

Generative AI creates new data based on learned patterns, unlike discriminative models that focus only on classifying or interpreting existing data. Models like Med-PaLM and Med-Gemini leverage this ability to generate content for analysis and diagnostic support.

enerative models focus on creating new data based on patterns learned from existing datasets, unlike discriminative models that only classify or interpret existing data.the primary function of generative AI in medical applications which generate synthetic images or text to support diagnosis and research producing synthetic data to augment and diversify medical research resources..Nguyen et al., 2025; Med-PaLM; Med-Gemini; Introduction section on Generative AI in Healthcare.

โจทย์ ปรนัย
What is meant by the term “model as a dataset”?

Model as a dataset refers to the concept of distributing the knowledge learned by a trained model rather than the original patient data, which preserves privacy while enabling others to use the model for downstream tasks.

This approach leverages model parameters as a proxy for the dataset, aligning with federated learning and privacy-preserving AI principles.

โจทย์ ปรนัย
Which statement correctly distinguishes physics-informed and statistical models?

Physics informed model are primarily rule based approaches incorporate biological or physical principles into their structure to guide predictions, whereas statistical models rely purely on data patterns.It's cooperate domain specific knowledge.

In synthetic dataset page 2 say about Two broad catergories 1. Physics- informed 2. Statistical model the next line is talk about Physics informed physics principle and explict contraints to generate realistics nd physically. expert knowledge and know physics laws(eg. fluid dynamics, tissue biomechanics , or radiation physics)

โจทย์ ปรนัย
According to the article, what does the “image generation trilemma” describe?

The article explains that modern generative models cannot maximize all three dimensions diversity, image fidelity, and generation speed at the same time. Improving one usually weakens another, which is why this constraint is described as the image generation trilemma.

This idea comes from foundational work in generative modeling (eg, diffusion models, GAN theory), where model capacity and compute limitations force trade-offs between sampling speed, variation in outputs, and realism of generated images.

โจทย์ ปรนัย
What is the Human Turing Test used for in medical image synthesis?

The Human Turing Test is used so doctors or imaging experts look at fake images and real images, then try to tell which is which. If they can’t tell, the synthetic image is good and realistic.

from Alan Turing’s test evaluating whether machine-generated output is indistinguishable from human-generated content.Adapted in medical imaging research (to assess perceptual realism, something numerical metrics cannot fully capture.

โจทย์ ปรนัย
Which of the following is NOT mentioned as a potential benefit of synthetic data in healthcare?

The article explains several benefits of synthetic data more variety, better privacy, easier sharing across centres, and helping training. But it never claims synthetic data can remove all bias forever. Bias can be reduced, but not fully erased.

Synthetic data can add diversity, making datasets broader can protect privacy since no real patient identity is shared. It can help research groups share data. It can support teaching by creating rare cases. But bias removal models learn patterns from real-world data that still contain bias.

โจทย์ ปรนัย
What is one major ethical concern associated with generative AI in medical imaging?

A key ethical worry in the article is that generative models might accidentally reproduce parts of real patient images. That risk makes privacy protection a major concern.If the model memorises examples instead of creating new patterns, it may output images that are too like to the real image. This can reveal sensitive details exactly what synthetic data is meant to avoid.

จากการเป็นตัวเเทนประเทศไทยเข้าร่วมงาน The Global Ethics fotum 2025 ทำให้คิดว่า Generative AI ในวงการแพทย์ต้องมี rigorous evaluation metrics ต้องคำนึงถึง ethical considerations ระวัง data leakage และ reproduction of real patient data Synthetic data มีประโยชน์ แต่ต้องระวัง risk of patient reidentification

โจทย์ ปรนัย
What regulatory precedent did the article cite for synthetic data technologies?

Synthetic MRIs were allowed by the FDA because they transform or enhance existing medical images without altering clinical meaning. This sets an reviewed under image-processing pathways required to show safety, consistency This helps guide future regulation of synthetic imaging tools.

The article points to this FDA decision as evidence that synthetic imaging can fit within current regulatory structures regulators evaluate them using risk-based medical device frameworks synthetic data tools must demonstrate fidelity, clinical stability, and non-harm

โจทย์ ปรนัย
What is the main purpose of the article?

The introduction explains that the article reviews similarities, differences, and limitations of ASCVD risk tools in China, Japan, and Korea, and why better models are needed for East Asians.

Based on population-specific risk prediction and model calibration principles risk tools must match the population they’re used on.

โจทย์ ปรนัย
Why might Western-based risk prediction models overestimate ASCVD risk in East Asian populations?

Western models are calibrated to higher-risk populations, so they overpredict when used in lower-risk East Asians.

Risk models must match the target population’s baseline event rate.

โจทย์ ปรนัย
What is the key advantage of the China-PAR model compared to Western-based models?

China-PAR is built from large, population-based Chinese cohorts, making its predictions better aligned with real ASCVD patterns in Chinese populations.

Risk models perform best when calibrated to the epidemiology, demographics, and baseline risk of the target population.

โจทย์ ปรนัย
Which of the following variables is not typically included in ASCVD risk prediction models discussed in the article?

The models described CMCS, China-PAR, PCE use standard clinical variables (age, BP, cholesterol, smoking). Genetic ancestry is not part of these equations.

ASCVD prediction models are built from large cohort data using established, measurable risk factors that show strong association with outcomes (e.g., CMCS and China-PAR equations developed from long-term Chinese cohort studies). These models prioritize factors with proven predictive power across populations, not genomic markers, which the article never includes.

โจทย์ ปรนัย
What is a major difference between the Suita Score and the Framingham Risk Score?

Suita Score is calibrated from Japanese cohort data, while Framingham is based on Western cohorts leading to differences in baseline ASCVD risk and model accuracy.

Risk models must match the target population’s epidemiology; locally derived data improves calibration and prediction.

โจทย์ ปรนัย
According to the article, what is a potential benefit of developing East Asia–specific risk models?

Western models (e.g., PCE) often overestimate ASCVD risk in East Asians because baseline incidence is lower. East Asia–specific models use regional cohort data, making predictions better calibrated for local populations.

Risk prediction works best when models are derived from the target population’s epidemiology.

โจทย์ ปรนัย
Which of the following models was originally developed for a Western population?

One could mistakenly argue that because the Suita Score includes risk factors commonly used in Western models.

If two models share similar clinical predictors, one might erroneously generalize that they share the same population origiื even though this is not true here.

โจทย์ ปรนัย
Which factor was highlighted as influencing ASCVD risk differences among East Asian countries?

Differences in habits like high-salt diets, physical activity, and lifestyle patterns lead to different ASCVD risk profiles across East Asian countries, so risk is not uniform.

Heart disease risk depends a lot on lifestyle and environment, so models must match the real-world behaviors of each population.

โจทย์ ปรนัย
What future direction does the article suggest for improving ASCVD risk prediction?

The article explains that future models should use AI together with local health and lifestyle data. This helps make the predictions fit East Asian populations better.

I looked at the article’s main message: Western models don’t fit East Asians well because their health patterns differ. So the article suggests improving accuracy by building future models that use AI plus local data. From this, the choice that matches that idea is

โจทย์ ปรนัย
Which statement best explains the key difference in how VAEs, GANs, and DDPMs generate medical images according to the figure?

In page 3 DDPMS DDPM builds images by removing noise step by step. VAE makes images through an decoder path, while a GAN learns by having a generator fight a discriminator.Their image its their create are making processes are clearly different.

In page 3 under the picture this is the answer because their show this picture Diffusion models follow the gradual denoising idea (Ho et al., 2020). VAEs rely on latent encoding and GANs use adversarial training

โจทย์ ปรนัย
Which of the following best explains the trend shown in Figure comparing age-standardized and crude CVD mortality rates among East Asian countries?

This explanation ties the lower mortality rates to population size rather than true health outcomes. While not fully accurate, it gives a simplified causal link that appears logical on the surface.

This answer follows a population-based reasoning pattern, assuming that crude and age standardized rates might shift with population size, this line of thinking can seem reasonable without deeper epidemiologic correction.