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

According to the introduction, the author aims to discuss the advancements, uses of generative AI, and it's ethical concerns, validity, and interpretability in medical imaging

The evidence for this answer is stated in the third paragraph of the Introduction, where the author is explaining what they will be discussing throughout the rest of the study.

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

Generative AI, unlike traditional dataset sharing which involves transferring actual images, they generate new synthetic images that are based of the original data.

This evidence is stated in the second paragraph of the Synthetic Datasets chapter, where the author discussed about how generative AI differs to traditional dataset sharing.

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

Generative AI would learn and store characteristics of the original data to generate new synthetic images based on that data. These synthetic datasets closely resemble the original data.

This evidence is provided in the Synthetic Datasets chapter, when the author introduces a new advancement in data sharing in generative AI models.

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Which statement correctly distinguishes physics-informed and statistical models?

Physics-Informed models are rule-based that generates data through mathmatical equations to generate realistic and accurate data. They provide higher interpretablity than statistical models.

This evidence is found in the Synthetic Datasets chapter, where the author discussed the two broad categories of generative models used to generate synthetic datasets.

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

The 3 corners of the trilemma represents one desirable property of the generative AI model: -Quality: how realistic, detailed, and accurate the images are -Diversity: the variety of images the model can produce -Speed: how quickly the images can be generated But improving one property would often come with the cost of another, making it a trilemma.

This evidence is presented in the Synthetic Datasets chapter, where the author includes an image of the image generation trilemma along with an explanation.

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

The Human Turing Test is an evaluation method used to measure how realistic AI-generated, synthetic medical images are, by testing whether expert human evaluators can differentiate between synthetic images and real ones. It ensures the data generated are valid and reliable for further uses.

This evidence is stated in the Human Evaluation chapter.

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

Medical biases are still one of the major concerns of using generative AI for medical imaging.

This evidence is found in the Challenges and Consideration chapter.

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

It's the generative AI's duty of confidentiality to the patient, where personal information or identity must be kept secret. AI could unintentionally produce private patient-identifiable details.

This evidence is found in the Challenges and Consideration chapter, where the author discussed about patient privacy and data copying.

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

The main regulatory precedent for the synthetic data technologies is the FDA approval of synthetic MRI tools as part of medical image-processing software, officially recognized as valid

This evidence is found in the third paragraph of the Future Directions chapter.

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What is the main purpose of the article?

This study investigates how well ASCVD risk protection models, developed using Western norms, apply to East Asian norms, specifically Chinese, Japanese, and Koreans. It identifies gaps, evaluate regional models, and provides solutions to improve risk prediction and clinical decision-making for East Asians.

This evidence can be found in the Abstract and Introduction of the study.

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

The Framingham Risk Score is the only model among the options that was developed for a Western population. All the others were designed for East Asian populations.

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

East Asians have a lower baseline incidence of ASCVD than Western norms, even when major risk factors like blood pressure, cholesterol, or smoking are similar. Models that are designed for Western populations should'nt be used for other populations due to different cultures, way of living, dietary, environment, genetics, economy, etc. This can result in overestimating or underestimating ASCVD risk in East-Asian norms

The main theoretical explanation is epidemiologic miscalibration, when a model designed for one population doesn’t work well for another because of differences in disease rates, genes, diet, and environment. This can lead to wrong predictions and poor medical decisions.

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

The key advantage of the China-PAR model is that it was developed specifically for China using nationally representative cohort data that reflect the geographic, socioeconomic, and lifestyle diversity, avoiding the over or underestimation seen with Western models.

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

The models discussed are all based on traditional clinical and demographic variables, not genetic data.

In the second paragraph of the ASCVD Risk Prediction in Japan chapter, the author never included genetic ancestry markers into one of the variables for ASCVD risk prediction models.

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

The Framingham Risk Score was developed in the United States, using mainly non-Hispanic White populations. When this Western model is used with Asian populations, including Chinese and Japanese, it overestimates CHD risk which is exactly why East Asian countries have created their own local models.

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

Using ASCVD risk models developed for Western populations may lead to over or underestimation of risk in East Asian populations due to difference in genetics, lifestyle, and other factors. East Asia-specific risk models can improve accuracy and ensure more appropriate clinical decision-making.

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

One of the key factors influencing ASCVD risk differences among East Asian countries is variation in cultural and dietary habits, especially salt intake, diet composition, alcohol use, and lifestyle behaviors, explaining why the baseline incidence of ASCVD risk varies across China, Japan, and Korea.

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

Multimodal AI approaches, combining imaging, biomarkers, and machine learning, are being explored to improve predicting accuracy and cost-effectiveness

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

The figure in Khosravi et al., The Lancet Digital Health (2025) compares how three major generative AI models—VAEs, GANs, and DDPMs—synthesize medical images, showing that each uses a fundamentally different generation mechanism.

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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?

Japan’s success is not merely a demographic artifact but rather reflects effective cardiovascular prevention, healthcare access, and population health management.

Crude mortality rate reflects the total number of deaths per population, affected by both disease risk and population age structure. Age-standardized mortality rate adjusts for differences in population age distribution, allowing fair comparison across countries. If both the crude and age-standardized mortality rates are low, it suggests that both the disease risk and healthcare outcomes are favorable as is the case for Japan and South Korea.