| 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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To create a new plan for forming a smart and healthy cities after the COVID-19 pandemic.
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City need a way to combine technology with health needs to handle future diseases and urban growth.
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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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Traditional AI mostly find patterns but the generative AI use early detection and prevention measures to stop health problems before it worsens.
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The article mentioned that while AI-driven predictive algorithms help with detection but the goal is to shift towards proactive health management.
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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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Sharing knowledge of the city model instead of raw private data.
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Protect privacy while also allowing different groups such as government or private companies to be able to work together using the same smart city 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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Physics-informed models looks at how real world actually works with the real biological, while statistical models looks at data patterns and numbers.
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The article suggests integrating climate and health data to create a proactive strategy which requires physic-informed model that understands the real world situation not just mathematical patterns.
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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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A struggle between keeping the data private, making it useful and easy to access.
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The need for strategic investment to share cost, benefit, and risks. Which it’s a problem of balancing different goals in city planning.
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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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Asking people if the smart city service feels helpful.
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The article mentions quality of life and how citizens perceive health risk. So if people believe the technology works then they are more likely to use it.
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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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Supporting medical education |
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Making health care fair for everyone and helping people learn how to stay healthy.
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Provide resources to underserved populations and also improve overall public health outcomes.
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| 8 |
What is one major ethical concern associated with generative AI in medical imaging?
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Data copying and patient reidentification |
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Ai might accidentally copy a real person features, making it possible to identify them.
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Citizens must perceive technology as safe and helpful to trust it, data privacy is a foundation of this trust.
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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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Official government approval allows AI made images to be used like normal medical software.
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It suggests that for a healthy city, new technology must be officially integrated into medical workflow to be resilient.
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| 10 |
What is the main purpose of the article?
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To compare and evaluate ASCVD risk prediction models in East Asia |
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The study looks at how different tools perform for Asia populations.
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The article focuses on using ICT and AI to monitor health risks specifically to improve the quality of life in local populations.
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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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The framingham risk score is built for western populations based on data from long-term studies. While other options are specifically developed for Asian populations.
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Models must reflect the specific demographics they were built from to be accurate.
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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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Western models are built base by western populations where heart disease are common. However, Asians have fewer heart attacks at the same risk levels, Western math overestimates their actual risk.
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Health tools must be adjusted to the specific biological baseline of the local population.
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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 was calibrated using national data representing diverse regions in China |
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Models like China-PAR are better as they calibrated using data from local Chinese people.
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Health risk perception and quality of life are tied to local culture context and city specific data.
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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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Standard ASCVD calculator use basic clinical data such as age, blood pressure, cholesterol, and smoking status. Genetic ancestry markers is still a current research topic not a standard component of the tools.
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Most global health models prioritize measurable clinical biomarkers over complex genomic data.
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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 suita score is superior for Japanese patients since it was built using data from the local Japanese community rather than American.
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Resilience is built by using data that reflects the actual lifestyle and health risks of the local community.
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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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using a tool made specifically for Asia, gives a much more correct answer and stops the tool from guessing that a person’s risk is higher than it really is.
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Using population specific tools provides a better value proposition for the health of citizens.
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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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Differences in heart disease risk between Asian countries are often caused by local habits such as high salt consumption in certain diets.
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A healthy city should focus on local cultural and dietary key activities that influence public health.
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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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Health predictions using AI that combines many data types with specific local environmental regional information.
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Integrating diverse data sources through ICT creates a more resilient and personalized health system.
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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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DDPMs iteratively remove noise through reverse diffusion rather than using encoder–decoder or discriminator structures. |
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DDPMs function through unique processes of forward diffusion then learning to recover the image by reverse diffusion.
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Diffusion probabilistic modeling, unlike VAEs or GANs, the DDPMs can treat images generation as a reverse diffusion while other can’t, they can only go forward diffusion.
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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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Despite differences in age structures, Japan maintains low mortality rates in both measures, suggesting effective prevention and healthcare systems. |
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In graph A, Japan has the lowest rate while the graph B shows Japan rate being higher than its age standardized rate. The comparison indicates that when removing standardized, Japan healthcare and prevention strategies are highly effective.
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Age standardized rates allow for a comparison between countries with different aging demographics.
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