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

According to the introduction, the artical aims to review recent advancements in technology and generative AI in medical imaging, particularly between South East Asian natives living in their countries and South East Asian immigrants in The United States.

Page 1, first paragraph, the article states that epidemiological characteristics of ASCVD vary within East Asian people compared with Southern, Western and Central Asian people. Even differences within East Asian sub-populations exist, highlighting the need for better advancement in generative Ai in medical imaging.

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

In the healthcare system, generative AI differs from traditional discriminative models because ot can generate new data rather than inly classify or interpret existing data.

Generally, generative models learn data distributions and create new samples, while discriminative models mainlt perform classification and prediction tasks.

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

Model as a data set typically means using or sharing a trained model that has learned information from the orginal data, rather than sharing the raw data itself. This can help protect patient privacy while still allowing researchers to benefit from the knowledge contained in the data.

In medical AI research, trined model weights can capture patterns from datasets and be shared as an alteto distributing sensitive patient data. This approach supports pirvacy preservation and data accessibility.

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Which statement correctly distinguishes physics-informed and statistical models?

Physics-informed models use known biological or physical laws during training and image generation, while statistical models primarily learn patterns from data without incorporating these principles.

Physics-informed AI combines domain knowledge such as imaging physics or biological constraints, with data-driving learning. However, statistical models rely mainly on statistical relevance found in the trainung data.

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

The image generation trilemma refers to the challenge of balancing three goals including generating diverse images, maintaining hugh image quality and achieving fast generation speed. However, improving one aspect often comes at the expense of another

Different image generation models cannot simultaneously maximize image quality, diversity and generation speed, demonstrating the image generation trilemma.

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

In medical training, a human turing test invloves asking expert clinicians to distinguish between real medical images and those genrated by AI which are synthetic images. If the experts cannit reliably tell the difference, the synthetic images are considered to have high visual realism and clinical accuracy.

A human turing test ks widely used to evaluate synthetic medical images. When experienced clinicians cannot reliably distinguish AI-gienerated images from real patients images, it proves evidence that the genrated images have achieved a hugh level of visual realism and preserve clinically relevant features.

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

While synthetic data is a powerful tool in healthcare, it cannot permanently eliminate all biases. One reason is that synthetic data is often generated based on real-world datasets. If the original data contains biases, the synthetic version will most likely replicate or even amplify them.

Since synthetic data is generated from real datasets, any bias present in the original data can be inherited by synthetic data, which may affect the fairness and accuracy of AI systems.

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

Generative AI models trained on medical images can inadvertently memorize specific patients data. This poses a major ethical risk as it may allow unauthorized individuals to reidentify patients from genrated images, breaching privacy regulations.

Studies have demonstrated that generative AI models can sometimes memorize and reproduce examples form their training data. This creates a risk that patient-specifc features could be unintentionally revealed in genrated images, potentially compromising patient privacy and confidentiality.

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

FDA clearance of synthetic MRI as image-processing software uses AI and synthetic data to generate multiple image contrasts from a single MRI scan.

This technology demonstrates that AI-based image-processing tools can be sadelynapplied in clinical practice. By generating multiple MRI image contrasts from a single scan, the technology reduces scanning time while maintaining diagnostic information, highlighting the practical value of synthetic data in AI medical imaging.

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What is the main purpose of the article?

This article compares possible risks of ASCVD in South East Asian natives and immigrants, as well as its prediction models used in particularly 3 countries namely China, Japan and Korea.

According to the article, some countries results do not line up with immigrants’ results, highlighting inaccuracy and limitations of datasets. It also highlights a significant need in AI models to accurately evaluate and diagnose ASCVD patients in South East Asia natives and immigrants.

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

It is developed based on data from the Framingham Heart Study, which began in Massachusetts, USA. It is the foundation model for cardiovascular risk assessment in Western populations.

Other answers such as the Korean Risk Prediction Model was made and based on Korea, particularly used in Korean citizens.

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

Prediction models buil in western countries may have limitations when it comes to data set of Asian specifically South East Asian.

The article clearly states that Western countries specifically the USA does not specify small ethnic groups when it comes to the Asian population in prediction models.

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

China-PAR was developed using large-scale Chinese cohort data, making it more accurate for predicting risks specifically for Chinese individuals.

We can tell from the differences that Western-based models werre developed using predominantly Caucasian people as it often overestimate cardiovascular risks when applied to Asian populations dues to different genetic profiles.

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

Traditional ASCVD risk models focus on clinical and lifestfactors that have been extensively validated in large longitudinal studies.

Genetic Ancestry Markers are an active area of research, they are not typically included in the standard clinical risk calculaters used in routine practice.

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

The Suita Study was specifically developed using data from cohort study in Suita, Japan, to more accurately predict coronary heart disease risk for Japanese people.

While both predict 10-year risk, the Suita Score includes different parameters such as specifically accounting for nin-HDL cholesterol and proteinuria, reflecting the specifc health profiles of the Japanese population.

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

East-Asia models account for different prevalence rates of risk factors, leading ti mire precise individual assessments.

By reducing overestimation, these models help prevent unnecessary medical interventions and focus resources pn those truly at high risk.

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

Dietary variations includes habits like high sodium intake, which correlates to hypertension. Cultural practices patterns in each country differs, leading to different outcomes in ASCVD risks.

It is evident that habits and practices among the South East Asian population differs, leading to different circumstances and outcomes in ASCVD risks and predictions.

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

Modern medical advancements focus on tailoring risk models to specific populations and regions to address disparities and improve accuracy compared to generalized global models.

Evidence shows that population-specific models generally provide more accuand equitable healthcare predictions.

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

VAES output a clear image of the X-ray while GANS output and answer of whether the image is fake or real.

It is evidently clear that the outcome of each models is different, therefore the image or answers are different.

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

It is clear that from the bar graph, Japan and South Lorea have significantly lower mortality rates compared to Mongolia and North Korea.

Japan maintains relatively low crude mortality rates compared despite having ine of the world’s oldest populations.