| 1 |
What is the primary goal of the article according to its introduction?
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To explore advancements, applications, and challenges of generative AI in medical imaging |
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Because the article focus mainly on how to further improve medical cares by enhancing the quality of medical equipments.
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The introduction of the text implies the needs to compare different aspects and the possibility of developing AI in several countries around the world in order to further develops medical tools or equipment using AI.
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| 2 |
How do generative AI models differ from traditional discriminative models in healthcare applications?
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Generative models produce new data rather than only classify or interpret |
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Generative AI are meant to generate new data from medical notes unlike traditional discriminative that only focus on classifying or diagnosing the data.
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Based on their usage, generative models study how to produce a new set of data.Traditional discriminative, on the other hand, focus on estimating the result of a certain topic. The use of probability theory and statistical learning theory were included in these types of AI.
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| 3 |
What is meant by the term “model as a dataset”?
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Sharing trained model weights instead of raw data |
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Trained model created by learning, instead of raw data, was built to help or do specific tasks since the word “dataset” means gathering up information with similar structure.
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Dataset uses mathematical and computational modeling to calculate and develop the tools.
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| 4 |
Which statement correctly distinguishes physics-informed and statistical models?
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Physics-informed models incorporate biological or physical principles |
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The types of usage differs in both of these categories of models because physics-informed model involve the use of physical principles or laws with the data; however, statistical models focus on observing or learning data and patterns.
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The name of these data’s implies its use and tasks.
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| 5 |
According to the article, what does the “image generation trilemma” describe?
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Trade-offs among image diversity, quality, and speed |
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The image generation trilemma is the 3 requirements AI should achieved when it comes to generating images which is the velocity when generating, the quality of the picture, and the variety of the images.
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The generative learning trilemma was written in a research called “tackling the Generative Learning Trilemma with denoising diffusion GANs” to increase the ability to create images by AI.
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| 6 |
What is the Human Turing Test used for in medical image synthesis?
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To assess realism of synthetic medical images by experts |
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The visual Turing test was created in order the distinguish and detects real medical images and synthetic medical images for the medical experts.
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The standard accuracy formula was used in order to predict the type of images.Apart from this, there’s an existence of supporting data such as “Welch’s test” that compare the mean accuracy.
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| 7 |
Which of the following is NOT mentioned as a potential benefit of synthetic data in healthcare?
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Eliminating all medical biases permanently |
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Synthetic medical data were created to help people in terms of medical education. Eliminating medical biases “ permanently” is almost impossible although it does softened the bias.
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Because it’s an artificial data structure, the risk of people falsely assuming the data is very high leads to the possibility of overlooking the clinical validations , making it not a permanent solution.
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| 8 |
What is one major ethical concern associated with generative AI in medical imaging?
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Inability to generate realistic images |
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Pictures generated by AI aren’t always trustworthy since there might be certain unreal features.
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A framework called the “GREAT PLEA” was produced to focus on the concerns of images inaccurately depicted by AI generator.
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| 9 |
What regulatory precedent did the article cite for synthetic data technologies?
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FDA clearance of synthetic MRI as image-processing software |
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It was mentioned in the article about how synthetic MRI technologies produces images that has a higher quality and reduce scan times.
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Mathematical foundation such as Virtual scanning and instant synthesis was used to describe how this technology worked.
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| 10 |
What is the main purpose of the article?
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To introduce new diagnostic imaging technologies |
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The article focus on how AI image generator can plays a major role in medical research and development.
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Several innovations and features were written to show the improving performance of medical equipments.
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| 11 |
Which of the following models was originally developed for a Western population?
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Framingham Risk Score |
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Framingham Risk Score is the only model that was originally developed in The United Sates for white American cohort.
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It was the only model in the choices that was for western people and was used to calculate the risk of CHD by analyzing different factors and variables of a particular person.
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| 12 |
Why might Western-based risk prediction models overestimate ASCVD risk in East Asian populations?
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East Asians have lower baseline incidence of ASCVD |
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Average East Asian populations are tend to have different body proportion and sizes compared the western population, making it assumes a higher percentage of risk in Asian than Western populations.
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The tissues properties in humans from different parts of the of the world is different, differing certain characteristics among the people.
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| 13 |
What is the key advantage of the China-PAR model compared to Western-based models?
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It includes both genetic and lifestyle factors |
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China - PAR has its own unique approach which is including family history or personal habits that could leads to the disease.
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It highlights the differences and similarities between people with different backgrounds and ethnicity.
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| 14 |
Which of the following variables is not typically included in ASCVD risk prediction models discussed in the article?
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Genetic ancestry markers |
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Genetic ancestry markers wasn’t mentioned in the article since it isn’t the foundation of the risk prediction unlike others.
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Because other models are used instead of it as a proxy for genetics.
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| 15 |
What is a major difference between the Suita Score and the Framingham Risk Score?
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Suita Score was designed for a Japanese population using local epidemiological data |
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The target population of suitable score is mainly for East Asian populations. Whereas, Framingham risk score focus on western populations,
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Since it was mentioned that the body proportions of the population in western countries and Asian counties in different, the type of model used to determine the risk also differs ,
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| 16 |
According to the article, what is a potential benefit of developing East Asia–specific risk models?
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They improve accuracy and reduce overestimation of risk |
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East Asia specific risk models enhanced the diagnostic accuracy compared to Western tools.
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FRS overstimulates the risk in East Asian unlike East Asian specific that ensure the realistic information.
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| 17 |
Which factor was highlighted as influencing ASCVD risk differences among East Asian countries?
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Cultural and dietary variations, such as salt intake and lifestyle |
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The rate of sodium intakes and lifestyles in each country is different.
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The cultures and traditions in each country is not the same, making the risk different.
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| 18 |
What future direction does the article suggest for improving ASCVD risk prediction?
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Using multimodal AI-based prediction integrated with regional data |
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AI based information and prediction will be used in the future to help with medical decisions.
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In the future, the development of AI and usage of it will soon be adapted and become a crucial part of diagnosis.
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| 19 |
Which statement best explains the key difference in how VAEs, GANs, and DDPMs generate medical images according to the figure?
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GANs explicitly use noise diffusion steps to reconstruct input data. |
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| 20 |
Which of the following best explains the trend shown in Figure comparing age-standardized and crude CVD mortality rates among East Asian countries?
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China’s lower crude mortality rate compared to its age-standardized rate indicates overestimation of CVD prevalence. |
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From the graph, the number compared to the actual mortality rate is different .
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