| 1 |
What is the primary goal of the article according to its introduction?
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3. To explore advancements, applications, and challenges of generative AI in medical imaging |
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I've read the article, it stated many facts about generative artificial intelligence helping in the hospital(image creating) like for example it shown the thinking of these process, and what people might be concern about, so we are kind of like exploring it
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I find the conclusion(future direction) and look through the abstracts of the text(first paragraph of the fist page)
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| 2 |
How do generative AI models differ from traditional discriminative models in healthcare applications?
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2. Generative models produce new data rather than only classify or interpret |
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I saw a text saying, it help students get more rare cases, because the generative AI models can create situation too, there's also a text that said something about trying to develop this model so they don't only interpret the data
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I found it of like the second page saying they don't want it to be mimicking the statistical original data and somewhere around that last page too about developing the model
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| 3 |
What is meant by the term “model as a dataset”?
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4. A database of patient histories |
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they can't have evidence if there isn't a case yet, the model couldn't generate a data that hasn't been found/studied, so the patient history is the main recourses, that's also why some people that gave away there persona information for this researches are quite concern about their privacy
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found this in page 7 last paragraph that say "patient privacy and data copying"
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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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it is said that physics informed models are rule base, that incorporate domain specific knowledge and physics principle rather than learning is pattern data
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found this in page 2, 3rd paragraph
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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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In page 2, 4th paragraph we see that the statistical model learn from pattern and they have basically 3 types of pattern they followed/can do, the VAEs,GANs, and DDPMs which later show the 4 page figure 2 their image generation trilemma
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you can find this in page 4, figure 2, where they show you a triangle, this triangle describe the abilities, and traits off of image generation trilemma. You can also find this in page 3 figure 1 where they show you the ability of each image generation trilemma
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| 6 |
What is the Human Turing Test used for in medical image synthesis?
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2. To assess realism of synthetic medical images by experts |
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It is said that, they started the human turing test involving experts distinguish real image from synthetic ones, offering insights into the perceptual quality that is important for medical use
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found in page 4, health care specific metrics
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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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3. Facilitating multi-centre collaborations |
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it is not said in the text
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it is not said in the text
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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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out of the 3 concern, they state that privacy for patient is concerning, because it contain patient identifying information embedded within the pixel values, posing unique challenges for anonymisation
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you can find this in page 7, challenges and considerations
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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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east asian people relay on western and inaccurate information that might overestimate or underestimate than their current situation, that's why this article is showing us the researches to make an accurate and specific tests for people in east asian
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Most of the text talk about how east Asian relay on western information, but to be specific in page 5, the distribution in BMI between asian and western people cut off for obesity is different
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| 11 |
Which of the following models was originally developed for a Western population?
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5. NIPPON Data80 Model |
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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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they compare the risk of east asian to no-hispanic white and turns out the east asian has everything less than than non-hispanic white including, hypertensions, diabetes, diabetes, dyslipidemia, and obesity, these thing involve with cholesterol levels
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said in the 6th page where they compare the risk
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| 13 |
What is the key advantage of the China-PAR model compared to Western-based models?
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1. It includes both genetic and lifestyle factors |
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the china-par make this data to estimate risk in ASCVD in chinese people
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shown in page 9 in paragraph 1
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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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3. Serum cholesterol |
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look through each predictions tool from each east asian nations, for example this one in korean, there is not serum choresterol messured
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found in table 3 ASCVD risk assessment tools in korean
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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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The suita score is in the japan prediction topic
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found this in ASCVD risk prediction in Japan about paragraph 2
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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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3. They improve accuracy and reduce overestimation of risk |
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east asian relay on information from western nations which are inaccurate, the reason they(east asian country) create their own prediction was to make it more accurate for their own people
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You can find this in the abstrast, future direction and conclusion
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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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all of the choice seems to be downgrading the system/predictions even more and for predictions on this ASCVD diseses you can't just focus on one thing , too big of an image or too small, specific details
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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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3. DDPMs iteratively remove noise through reverse diffusion rather than using encoder–decoder or discriminator structures. |
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We see in the 2nd page 4th paragraph that DDPMs reduce noise from the picture, we can also see that in the picture shown above
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you can see each of their abilities explain on the second page 4th paragraph
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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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3. Despite differences in age structures, Japan maintains low mortality rates in both measures, suggesting effective prevention and healthcare systems. |
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it is explain in the graph that japan has the lowest mortality rate
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