| 1 |
How does the concept of “model as a dataset” reshape traditional data-sharing practices in medical imaging?
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3. It enables sharing of learned model weights instead of sensitive raw images. |
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“Model as a dataset” allows researchers to share trained models so others can use their knowledge without accessing patient images, protecting privacy. Other options are wrong because it does not remove regulations, replace data with text, require open repositories, or limit reuse. |
Sharing model weights instead of raw data maintains the benefits of collaboration and training while minimizing privacy risks, which would improve ai training in the fututre. |
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| 2 |
Which analytical conclusion can be drawn about the trade-offs between physics-informed and statistical models?
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2. Physics-informed models are more interpretable but computationally intensive. |
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Physics-informed models can be train using biological or physical laws, making their predictions easier to interpret, but this often increases computational cost. |
Incorporating domain knowledge improves interpretability and reliability, while statistical models rely on data patterns, creating a trade-off between insight and computational cost. |
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| 3 |
Why is “mode collapse” considered a critical problem in GAN-based medical image synthesis?
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2. It reduces image realism and variety by producing repetitive outputs. |
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Mode collapse occurs when a GAN produces limited or identical images and failing to capture the full diversity of the dataset. |
In medical imaging, diverse and realistic synthetic images are crucial for training, augmentation, and model validation, if GAN-based medical image have Mode collapse the realism of the image would be reduce, which cause a problem, because the discriminator would feed low realism image back to the generator, which would cause a huge error quality when the generator generate image again. |
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| 4 |
Why are healthcare-specific metrics preferred over general-purpose metrics such as FID or SSIM?
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2. They better capture clinical accuracy and diagnostic relevance. |
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Healthcare-specific metrics assess medical relevance and feature correctness, which is better than FID or SSIM, which have lower accuracy. |
In medical imaging, an image can look realistic, but it may not be accurate, healthcare-specific metrics provide both accuracy and diagnostic relevance. |
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| 5 |
What does the article identify as the key tension between privacy preservation and image fidelity?
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1. Higher realism may risk reproducing identifiable patient data. |
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there is a risk that they reveal patient-specific features, creating a privacy concern. |
There is a trade-off between realism and privacy improving image fidelity create better image for training and diagnosis but increases the risk of exposing sensitive patient information, which is unethical. |
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| 6 |
Why is the FDA’s approval of synthetic MRI technology significant for future AI-generated data?
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1. It establishes a framework for validating synthetic data equivalence in clinical use. |
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FDA clearance shows that synthetic MRI can meet safety standards. |
These technologies were regulated as image processing software rather than as completely novel modalities, with the FDA requiring extensive clinical validation to show that the diagnostic performance of the radiologist remained equivalent when using synthetic images versus conventional images. |
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| 7 |
Which strategy would best mitigate demographic bias in generative models according to the article?
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1. Increasing sampling from majority populations |
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The article mention that bias arises when models underrepresent certain groups. If we increase sampling the ai would not have any bias. |
Generative models reflect the data they learn from, if we give them more sampling ai could have fairness and diversity. |
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| 8 |
How do DDPMs exemplify versatility in healthcare image synthesis?
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2. They can perform multiple tasks such as denoising, inpainting, and anomaly detection without retraining. |
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DDPMs are flexible generative models that can handle various image synthesis tasks using the same trained model. |
DDPMs generate data by learning to reverse a noising process. The model starts with a sample from a simple distribution (eg, Gaussian noise) and iteratively denoises the sample using a learned Markov chain. At each step, the model estimates the gradient of the data distribution and refines the sample accordingly. By repeatedly applying this process, DDPMs can produce high-quality samples that closely resemble the training data. The figure depicts the forward diffusion process that gradually adds noise to the data and the reverse diffusion process that progressively denoises the sample to generate a clean output. |
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| 9 |
What analytical insight does the article provide about integrating AI-generated medical images into education and research?
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2. It enhances training by providing diverse, realistic datasets without ethical breaches. |
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The article notes that AI-generated images allow students and researchers to practice on realistic diverse data while avoiding patient privacy concerns. |
Using AI-generated medical images in education and research improves learning for the students while maintaining ethical compliance and privacy. If the real patients image was use, their would be ethical concerns and privacy data leaking. |
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| 10 |
Why is regional calibration essential when applying risk prediction models across countries?
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2. To adjust for population-specific incidence and lifestyle differences |
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Risk models developed in one country may over- or under-estimate risk when applied elsewhere due to differences in baseline disease rates, diet, lifestyle. |
Regional calibration ensures predictions reflect the true cardiovascular risk in a population, because it the doctors could calculate the risk more easy. |
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| 11 |
What analytical conclusion can be drawn when comparing the China-PAR and Framingham models?
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2. China-PAR uses local epidemiological data, leading to improved predictive validity. |
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China-PAR uses local epidemiological data, which increase predictive validity making it more accurate for asia contries , compare to Framingham models overestimates risk in East Asians. |
China-PAR mention the use of population-specific data that improves model reliability by reflecting local incidence, risk factors, and lifestyle.
https://www.scopus.com/pages/publications/84991451186?inward= |
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| 12 |
Based on CVD mortality data, what analytical inference can be made about Japan’s position compared to neighboring countries?
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1. Japan’s low CVD mortality suggests effective prevention and healthcare systems. |
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Japan shows lower cardiovascular mortality than neighboring countries, indicating successful prevention, early detection, and treatment strategies. |
Low CVD mortality reflects strong public health measures and healthcare quality. |
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| 13 |
What analytical limitation arises when using Western-derived coefficients in East Asian models?
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2. It introduces systematic overestimation of ASCVD probability. |
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Western-derived coefficients assume higher baseline cardiovascular risk, so applying them to East Asian populations overestimates individual risk. |
Risk models must reflect population-specific incidence, characteristics, environment and lifestyle. Inaccurate risk create overtreatment. |
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| 14 |
What policy implication can be derived from country-specific risk models?
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1. They allow for targeted national prevention programs. |
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Country-specific models provide accurate risk estimates for the local population, which reduce risk of overestimates risk. Reducing risk when using Framingham models |
Accurate, region-specific risk prediction guides policymakers in designing effective prevention strategies |
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| 15 |
If a model excludes socioeconomic variables, what analytical consequence might occur?
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2. Ignored non-biological determinants of disease |
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socioeconomic variables mean the model fails to account for factors like income, education, and access to care, which influence ASCVD risk |
Socioeconomic variables should not be ignore in third-world countries and developing countries, because it could cause underestimation or misclassification of risk. |
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| 16 |
How might AI improve next-generation ASCVD risk prediction in East Asia?
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2. By integrating multimodal data, including imaging and lifestyle information |
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AI can combine clinical, lifestyle, imaging, and genetic data to create more accurate and personalized risk predictions. |
AI could store all the user privacy information like clinical, lifestyle, imaging, and genetic data like how Palantir did, then AI could give the doctors to calculate risk for that patient. |
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| 17 |
What conclusion can be drawn from comparing Mongolia’s and South Korea’s CVD mortality rates?
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1. Mortality differences reflect varying effectiveness of national prevention programs. |
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The article shows South Korea has lower CVD mortality than Mongolia, indicating more effective prevention, early detection, and treatment. |
Comparing mortality across countries highlights how public health interventions and healthcare infrastructure, if mortality rate are high, the health interventions and healthcare infrastructure still have a problem or failed. |
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| 18 |
What is the most logical future direction for improving ASCVD models across East Asia?
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1. Establishing multinational data-sharing platforms to harmonize regional models |
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Sharing data across countries allows models to capture trends, improving accuracy and generalizability. |
Collaborative data sharing enables anyone to access the data would increase population-specific calibration, integration of diverse risk, and increase accurate research in risk disease in the future. |
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| 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?
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2. GANs provide a balance between image quality and diversity but may suffer from mode collapse. |
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GANs provide a balance between image quality and diversity but may suffer from mode collapse. While other such ass DDpms and VAEs prioritise high-quality and VAE prioritise diverse sample. |
Paper mention that, The image generation trilemma, which represents the trade-offs between three key aspects of generative models: diversity, quality, and speed
VAEs excel in generating diverse samples quickly but can compromise on image quality. GANs strike a balance, providing good quality and diversity but can suffer from mode collapse, thereby restricting the diversity. DDPMs prioritise high-quality and diverse samples at the cost of a slow generation speed. |
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| 20 |
Based on Figure, what analytical conclusion can be drawn regarding the distribution of cardiovascular disease (CVD) subtypes across East Asian countries?
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2. Stroke dominates as the primary cause of CVD death in all East Asian countries equally. |
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Every East asian country have stroke as the most CVD death |
The impact vary by country due to differences in diet, lifestyle, genetics, and healthcare systems |
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