| 1 |
What is the primary goal of the article according to its introduction?
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Exploring the potential pf generative artificial intelligence in medical image synthesis are models trained on differential diagnose on the basic of patient symptoms and test results.
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By generative artificial intelligence is a class of deep learning models capable of creating content that diverges from traditional discriminative models focused on interpretation or decision making.
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| 2 |
How do generative AI models differ from traditional discriminative models in healthcare applications?
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3. Generative models require manual image labeling |
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Generative artificial intelligence is a class of deep learning traditional discriminative models focused on interpretation or decision making.
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| 3 |
What is meant by the term “model as a dataset”?
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3. Sharing trained model weights instead of raw data |
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Model such a DALL-E
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| 4 |
Which statement correctly distinguishes physics-informed and statistical models?
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3. Physics-informed models incorporate biological or physical principles |
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Two broad categories of generative models provide the ability to generate synthetic datasets: pysics-informed and static models. Physics-informed models are primarity rule-based approaches that incorporate domain-specific knowledge and physics principles through mathematical equations and explicit constraints to generate realistic and physically plausible data.
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| 5 |
According to the article, what does the “image generation trilemma” describe?
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2. Trade-offs among image diversity, quality, and speed |
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Which represents the trade-offs between three key aspects of generative models:diversity,quality and speed.
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| 6 |
What is the Human Turing Test used for in medical image synthesis?
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5. To detect plagiarism in datasets |
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Human evaluation in addition to computational metrics, human evaluation remains a gold standard for assessing the quality of generated medical images. The human Turing test involves domain experts who are asked to discern between real and derived medical images.
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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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4. Eliminating all medical biases permanently |
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Synthetic data generation and image generation models hold immense promise for the future of medical imaging research. By leveraging the power of generative models, researchers can unlock unprecedented levels of data diversity, privacy preservation and multifunctionality, changing the way dataset creation, utilisation, and disease modelling are approached.
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| 8 |
What is one major ethical concern associated with generative AI in medical imaging?
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2. Data copying and patient reidentification |
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Although synthetic datasets can help to preserve patient privacy by generating anonymised data,concerns regarding potential data copying still exist. If a generative model is trained on a specific dataset cam replicate images that closely resemble the original data, then the model might inadvertently reveal sensitive patient information.
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| 9 |
What regulatory precedent did the article cite for synthetic data technologies?
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2. FDA clearance of synthetic MRI as image-processing software |
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| 10 |
What is the main purpose of the article?
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2. To compare and evaluate ASCVD risk prediction models in East Asia |
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The similarities and differences in the prevalence of ASCVD and its risk factors among Chinese,Japanese and Korean people living at United states.
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| 11 |
Which of the following models was originally developed for a Western population?
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1. Framingham Risk Score |
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| 12 |
Why might Western-based risk prediction models overestimate ASCVD risk in East Asian populations?
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4. East Asians have higher cholesterol levels |
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| 13 |
What is the key advantage of the China-PAR model compared to Western-based models?
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2. It uses imaging biomarkers only |
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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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Age |
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Risk Factors include alcohol use , blood pressure, body mass index, cholesterol, chronic kidney disease, diabetes mellitus , family history of CAD , physical activity and tobacco use
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Central illusation East Asian Cardiovascular Risk Calculators
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| 15 |
What is a major difference between the Suita Score and the Framingham Risk Score?
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2. Suita Score was designed for a Japanese population using local epidemiological data |
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In the 2017 JAS guideline, the Suita score accurately estimate the absolute CHD by incorporating demographics and risk factors including age , sex , smoking , blood pressure level , HDL-C, LDL-C, impaired glucose tolerance, and family history of premature CHD. Suita score was chosen from 10 different published risk prediction scores in Japan where internal validation fully performed.
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According to the article, what is a potential benefit of developing East Asia–specific risk models?
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3. They improve accuracy and reduce overestimation of risk |
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| 17 |
Which factor was highlighted as influencing ASCVD risk differences among East Asian countries?
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2. Cultural and dietary variations, such as salt intake and lifestyle |
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| 18 |
What future direction does the article suggest for improving ASCVD risk prediction?
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2. Using multimodal AI-based prediction integrated with regional data |
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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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5. All three models use identical processes but differ only in output image quality. |
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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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2. Mongolia and North Korea demonstrate higher CVD mortality due to older population structures alone. |
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