| 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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it is stated that they are studying the implications of using ai in medical imaging,it's advancements and it's challenges
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the first paragraph of our paper states the advancements of common ai used in this field over the past few years
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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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it creates new things instead of simply mimicking and repeating what is fed into it
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mentioned in the paper we were given that the alan turning institute defined it as such
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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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they used models they have already trained to feed into the data set
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said in the paper
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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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they need to use the basic principles within the training of the ai
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it is said that the physics model is primarily rule based that means that it's not using only statistical data but also using basic principles of biology within it's function
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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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it is said that while there are benefits to using ai generative imaging there are major draw backs such as the quality or speed of which the image is generated
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it is said in the paper itself(figure 2 the 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 using experts to access the image to see whether the ai generated a realistic image indistinguishable from a real image
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it is said that the ai needs to be trained and assisted by an expert before it can be used
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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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ai generated imaging can be manipulated and amplified in a general direction by the one training the ai,while it lessens the bias it can still be used to change results to a certain degree
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stated within the paper in page 8
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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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it can try to use the image fed into it during training to recreate a similar while not perfect copy ,it can lead to inaccurate reasearch. it also might not be trained in privacy and might reveal patient information
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it is staed in page seven on the privacy and data copyright section,where concerns are raised
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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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it is said that while it is given clearance is stil under monitoring by the fda
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the paper said it during the future directions part ,mentioning also the ethical dillemma and possible prospects of this technology
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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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it's main study point was that they wanted to study about ASCVD risk predictions that might be accurate in the west is less accurate for east asian countries(mainly china(including hong kong and makao) ,japan and the replublic of korea(south korea)
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the introduction paragraph strongly highlights this point
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| 11 |
Why might Western-based risk prediction models overestimate ASCVD risk in East Asian populations?
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2. East Asians have lower baseline incidence of ASCVD |
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the genetics,lifestyles and enviroment in asia is vastly different to the west
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it is said so in teh prevalence and risk factors section
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| 12 |
What is the key advantage of the China-PAR model compared to Western-based models?
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4. It was calibrated using national data representing diverse regions in China |
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it used the china information while in the diverse regions of mailand china might differ ,it will not differ as much as chinese factors compared to western factors producing a more localized and genetically specific risk calculation model
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it is said within the paper that chinese specific information was used to calculate the risk factors
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| 13 |
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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while all the others are considered in most(but not all) risk prediction models serum chloresteral is often unused (more prefered to use total chloresteral)
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it can be seen when you take each model and how it works placed next to eachother
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| 14 |
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 desighned specifically for japanese people(it includes chronic kidney disease status which the Framingham model deos not include as a factor) also it better reflects the japanese populations generally lower chlorestaral levels
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when comparing the suita score and the framingham the different factor uses is greatly noticed
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| 15 |
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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it reduces over estimation which in the long term can help reduce the resources and money spent on further examination of the disease
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over estimation can cause people with low risks of the disease to flock to the hospitals to get checked up only to find no results wasting valuable resources
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| 16 |
Which of the following models was originally developed for a Western population?
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1. Framingham Risk Score |
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it is original and the starter for all calculation models
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the framingham study is the first heart disease study
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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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while genetically the people here is very similar korea china and japan have different lifestyles and salt intakes
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it can be seen when studying the factors of each group compartively
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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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while using localized data is prime for determining risk factors but what can be seen in some models can be lacking in some other models
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it is said in the future directions and conclusions section
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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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4. VAEs and DDPMs both depend on real-versus-fake discrimination to improve accuracy. |
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accuracy is very important for those two models
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said
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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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japan has a low age mortality suggesting that the ascvd is spread out rather than in one age group suggesting better overall health care
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it can be analyzed
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