| 1 |
How does the concept of “model as a dataset” reshape traditional data-sharing practices in medical imaging?
|
3. It enables sharing of learned model weights instead of sensitive raw images. |
|
it can also lesson the privacy infringement problem and less sensitive information will be revealed |
instead of using sensitive information they use the datasets as models that are already trained |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 2 |
Which analytical conclusion can be drawn about the trade-offs between physics-informed and statistical models?
|
2. Physics-informed models are more interpretable but computationally intensive. |
|
it thinks more like a human(or a specialist) would due to how it is trained,it's a lot easier to understand but a lot more energy is used in computing |
it's trained by a real human instead of just sets of data |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 3 |
Why is “mode collapse” considered a critical problem in GAN-based medical image synthesis?
|
2. It reduces image realism and variety by producing repetitive outputs. |
|
it can cause problems in results |
if less quality is produced sometimes it can cause results to differ from reality |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 4 |
Why are healthcare-specific metrics preferred over general-purpose metrics such as FID or SSIM?
|
2. They better capture clinical accuracy and diagnostic relevance. |
|
the more clinically accurate one picture is the more likely it can be used |
if it's just regular standard it might not be the best for diagnostic |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 5 |
What does the article identify as the key tension between privacy preservation and image fidelity?
|
1. Higher realism may risk reproducing identifiable patient data. |
|
if realism is too high it might identifiable to one specific patient but if it's too low it cannot be used |
realism is needed for clinical accuracy |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 6 |
Why is the FDA’s approval of synthetic MRI technology significant for future AI-generated data?
|
It establishes a framework for validating synthetic data equivalence in clinical use. |
|
it can help validate and give more weight to the photos |
the paper says this in a more profesional way |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 7 |
Which strategy would best mitigate demographic bias in generative models according to the article?
|
2. Applying diversity-aware training and fairness constraints |
|
if training is applied then it could help lessen the probability of someone steering the ai in this path |
it is said in teh potential biases section of the paper |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 8 |
How do DDPMs exemplify versatility in healthcare image synthesis?
|
2. They can perform multiple tasks such as denoising, inpainting, and anomaly detection without retraining. |
|
it does not need restraining from bias |
humans may have bias and restrictions,ai however do not |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 9 |
What analytical insight does the article provide about integrating AI-generated medical images into education and research?
|
5. It eliminates the need for patient participation in studies. |
|
traditionally more patients are needed however now less patients are needed as some can be replaced |
it is said in the future directions part |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 10 |
Why is regional calibration essential when applying risk prediction models across countries?
|
2. To adjust for population-specific incidence and lifestyle differences |
|
diferent populations have diferent risks and genetical complications |
as we can see in the risk comparation charts |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 11 |
What analytical conclusion can be drawn when comparing the China-PAR and Framingham models?
|
2. China-PAR uses local epidemiological data, leading to improved predictive validity. |
|
china par is extremely localized to chinese people compared to farmingham |
it is seen extremely well when put together side by side |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 12 |
Based on CVD mortality data, what analytical inference can be made about Japan’s position compared to neighboring countries?
|
1. Japan’s low CVD mortality suggests effective prevention and healthcare systems. |
|
there has to be some prevention and detection superiority compared to others |
drastically lesser mortality rates compared to other neighboring countrys is rare naturally so there is probably so human intervention |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 13 |
What analytical limitation arises when using Western-derived coefficients in East Asian models?
|
2. It introduces systematic overestimation of ASCVD probability. |
|
when using different coeficients in diferent models the most likely outcome would be oever estimation |
it is implied throughout the paper(not directly) |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 14 |
What policy implication can be derived from country-specific risk models?
|
1. They allow for targeted national prevention programs. |
|
they allow for national programs to specifically target the people with higher risks |
if the risk analysis is more accurate we could specifically target specific high risk groups without wasting resources on when the risk analysis machine is wrong |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 15 |
If a model excludes socioeconomic variables, what analytical consequence might occur?
|
2. Ignored non-biological determinants of disease |
|
then it ignores factors such as eating,pollution and social situations |
ignoring socioeconomic factors can cause innaccurate measurements of lifestyle |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 16 |
How might AI improve next-generation ASCVD risk prediction in East Asia?
|
2. By integrating multimodal data, including imaging and lifestyle information |
|
ai can help calculate the risks by using imaging of the patients body |
ai can do many things |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 17 |
What conclusion can be drawn from comparing Mongolia’s and South Korea’s CVD mortality rates?
|
1. Mortality differences reflect varying effectiveness of national prevention programs. |
|
korea has a much lower death rate as seen compared to the much higher healthcare standards when in comparision of mongolia |
korea is much more advanced and easier coverage when it comes in the medical field than mongolia |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 18 |
What is the most logical future direction for improving ASCVD models across East Asia?
|
1. Establishing multinational data-sharing platforms to harmonize regional models |
|
if information is shared we could improve each model |
each model can be targeted depending on it's pitfall |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 19 |
According to the “image generation trilemma” shown in the figure, what analytical conclusion can be drawn about the relative strengths of VAEs, GANs, and DDPMs in medical image synthesis?
|
2. GANs provide a between image quality and diversity but may suffer from mode collapse. |
|
it while it has less diversity it is still functional however mode collapse is likely |
said in the paper when comparing diferent models |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 20 |
Based on Figure, what analytical conclusion can be drawn regarding the distribution of cardiovascular disease (CVD) subtypes across East Asian countries?
|
2. Stroke dominates as the primary cause of CVD death in all East Asian countries equally. |
|
it dominstes over everything else |
i think that strokes are more common genetically in east asian people |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|