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

This is because in the third paragraph of the introduction stating that the article is to be used to analyse the advancements, applications, and challenges of this field.

"analyses the advancements, applications, and challenges of this field"

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

The writer states that generative model are able to generate data based on model weights which means the second option is correct.

in the second paragraph of "Generative models" : "generate new synthetic images" and "store patterns and characteristics of the original data ... (weights)".

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

The advancements in AI has introduced a new way to share data. It allows people to share data as trained model weights which is more efficient and still retain most of the information in the original data.

Second paragraph of "Generative models" : "introduces a new concept in data sharing" and "provides an efficient alternative" and "new synthetic images with properties similar to the original data."

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Which statement correctly distinguishes physics-informed and statistical models?

Physics informed model, according to the article uses physics principles and explicit constraints to generate realistic data instead of learning the pattern like a statistical model.

Forth paragraph of "Generative models" : "are primarily rule-based", "incorporate ... physics principle", "through mathematical equations and explicit constraints" and "rather than learning the patterns"

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

Each statistical model has its own good and bad such as variational autoencoders having a high generating speed and high diversity but could lack in quality.

Figure 2 showing VAEs or variational autoencoders on the side of the triangle that has "speed" and "diversity". Alongside "VAEs excel in generating diverse samples quickly", and "can compromise on image quality"

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

The human turing test is making experts distinguish between a real medical image and a generated medical image to see whether or not the image is realistic and high quality.

First paragraph of "Human evaluation" : "involves domain experts who are asked to discern between real and derived medical images", "provides insight into perceptual quality and realism"

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

The article states that synthetic data will be useful in every situation but "eliminating medical biases".

Under "Potentials and promises" : "ability to increase dataset size and diversity", "synthetic datasets offer a privacy-preserving solution", "potential of these models serving as virtual surgical planning tools and educational resources" Under "Summary" : enabling ... multicentre collaborations"

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

The writer states this as one of challenges as models that are trained on a certain set of data can recreate data that can be used to identify the patient.

Under "Challenges and considerations" : "reveal sensitive information" and "reproduce images that closely resemble the original data"

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

The writer states that the FDA has already allowed the clearance of synthetic MRI technologies while requiring clinical validation to show that the performance of the radiologist did not drop when using synthetic images compared to normal images.

Third paragraph of "Future directions" : "FDA's clearance of synthetic MRI technologies", "requiring extensive clinical validation", "performance of the radiologist remained equivalent when using synthetic images versus conventional images"

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What is the main purpose of the article?

The writer states that the article was to highlight the limitations of current models and summarize risk stratification approaches in East Asian countriies

First paragraph of "Abstract" : "highlight the limitations of current risk calculators" and "summarize risk stratification approaches"

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

All of the other options are stated in the article being made by one of the countries which is considered an East Asian country.

Under "ASCVD risk prediction in China" : "China-PAR project found that it had low discrimination ability and poor calibration" Under "ASCVD risk prediction in Japan" : "JAS ... used the NIPPON DATA80" and "In the 2017 JAS guideline, the suita score" "korean risk prediction model" shows that it is korean

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

The writer states that the the ACC models used in western countries often overestimate risk due to having been conducted in the west

Due to the paper stating that "ASCVD risk calculator, developed by the ACC/AHA, overestimates risk" shows that their lifestyle/diet isn't the same.

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

The table shows the author Yang et al. includes predictors for people in urban and rural environment which means that these were the sample size. This shows that the study was conducted diversely.

Table 1, "3rd line Yang et al and Xing, et al" : "urban/rural"

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

The writer states that the risk factors used in the model includes age, blood pressure, race, smoking, diabetes and more without the mention of serum cholesterol anywhere in the article. The china par model also uses family history of ASCVD in their calculation.

First paragraph of "Current State of ASCVD Risk Calculators for East Asian Populations" : "The risk factors used in this model include ... age, systolic blood pressure, ... smoking" In table 1, 3rd line "Yang et al and Xing, et al" : "family with history of ascvd"

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

The writer did not state anything about the other choices but did state that the Suita score uses HDL and LDL in their models

Under "ASCVD risk prediction in Japan" : "risk factors including ... HDL-C, LDL-C"

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

The writer states that the current models overestimate the risk of ASCVD due to the study sample being inaccurate thus meaning developing an east Asian specific model will allow for more accurate predictions

Under highlights "overestimate risk in Chinese, Koreans, and Japanese people" Under "Current state of ASCVD risk calculators for east Asian population" : "risk factors used ... race(non Hispanic white, African American and others"

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

The differences in risk is influenced by salt intake as the paper states Japanese people are more likely to get strokes due to having a high salt intake

Under "ASCVD risk prediction in Japan" : "Japanese people have a high incidence of stroke" In "Lifestyle and cardiovascular disease in Japan, 2011" : "associations between lifestyle and cardiovascular disease, higher sodium intake"

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

Current studies that takes into account socioeconomic factors have been seen to overestimate the risk so the removal should benefit the prediction model

Under "Prevalence of ASCVD risk factors in East Asian populations living in Asia and in the United States" : "after adjustment for ... socioeconomic factors, east asian were .. less likely to report hypertension ... and diabetes mellitus"

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

Denoising diffusion probabilistic models add noise to the picture and learn to remove noise from said picture to generate a clean output and doesn't use an encoder or a discriminator.

Under "Generative models" : "gradually adds noise to the data", "progressively denoises the sample to generate a clean output"

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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?

Mongolia and North Korea both have old infrastructure and could be seen as underdeveloped thus increasing the deaths from ASCVD as there is very little options they have.

North Korea shouldn't have modern medical equipment to treat ASCVD as effectively as other countries and Mongolia is a mountainous country that is hard to transport materials to.