| 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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• The concept of “model as a dataset” refers to treating a trained generative AI model itself as a data-sharing resource, rather than sharing the original patient images.
• Traditional medical imaging data sharing requires careful handling due to privacy concerns, regulatory restrictions, and patient consent, often making multi-center collaborations complex.
• By sharing trained model weights, researchers and institutions can reuse the model to generate synthetic images or perform inference without exposing sensitive raw data.
• This approach reduces privacy risks and enables broader collaboration while maintaining regulatory compliance. |
1. From Khosravi et al. (2025, The Lancet Digital Health): The article emphasizes that sharing trained generative models can circumvent direct data sharing, preserving patient confidentiality while enabling multi-center research.
2. From Nguyen et al. (2025, Atherosclerotic Cardiovascular Disease Risk Prediction…): While focused on clinical risk models, the article highlights the principle of model-based sharing for regional adaptation—i.e., a model trained on one dataset can be shared and applied elsewhere without exposing the original sensitive clinical data.
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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 integrate biological or physical principles (e.g., tissue mechanics, MRI physics) into the model.
• Pros: The model outputs are more interpretable because they respect known physical laws and anatomical constraints.
• Cons: Incorporating these principles makes the model computationally intensive due to complex simulations or constraints.
• Statistical models, in contrast, rely on data-driven correlations and often require less domain-specific physics knowledge. They can be faster but may be less interpretable and sometimes violate known physical constraints.
This highlights the trade-off: interpretability and realism vs computational efficiency and flexibility. |
• According to Khosravi et al. (2025), physics-informed generative models are particularly useful when prior knowledge of anatomy or imaging physics is critical, while statistical models excel in data-rich contexts with lower computational costs.
• This aligns with the principle of model selection based on domain knowledge and computational constraints in medical image synthesis.
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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 the GAN generator produces limited or identical outputs for different inputs, failing to capture the diversity of the training data.
• In medical imaging, this is critical because:
• Models must generate diverse and realistic synthetic images representing different patients, pathologies, or anatomical variations.
• Mode collapse can lead to repetitive images, reducing the utility of synthetic datasets for training AI models, validating algorithms, or educational purposes.
• Therefore, addressing mode collapse is essential to ensure high-quality, varied, and clinically useful synthetic images.
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• According to Khosravi et al. (2025), mode collapse is a major limitation in GANs for medical imaging, as it undermines the generation of diverse pathological or anatomical variations.
• This aligns with the principle that generative models in healthcare must preserve diversity to reflect real-world clinical variability, ensuring downstream model performance and reliability.
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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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• General-purpose metrics like FID (Fréchet Inception Distance) or SSIM (Structural Similarity Index Measure) are designed to quantify visual similarity or realism of images in a generic sense, often using features trained on natural images (e.g., ImageNet).
• In medical imaging, visual similarity alone is insufficient. Generated images must also:
• Preserve pathologically relevant structures
• Reflect accurate clinical features for diagnosis or training AI models
• Healthcare-specific metrics are tailored to evaluate clinical fidelity and diagnostic utility, ensuring synthetic images are not only realistic but also clinically meaningful.
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• According to Khosravi et al., 2025, healthcare-specific evaluation metrics (e.g., lesion detection accuracy, anatomical consistency scores) are crucial because standard computer vision metrics fail to capture subtle pathological or anatomical details.
• This aligns with the principle of task-specific evaluation in medical AI, emphasizing that metrics should reflect clinical relevance, not just visual similarity.
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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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• Generative models aim to produce high-fidelity synthetic medical images that are realistic enough for clinical use.
• However, increasing realism can inadvertently reproduce patterns from the original training data, potentially revealing identifiable patient information.
• This creates a key tension:
• Privacy preservation: Avoid leaking patient-specific information
• Image fidelity: Maintain realistic, diagnostically useful images
• Achieving a balance requires careful design of privacy-preserving generative methods, such as differential privacy, federated learning, or sharing model weights instead of raw data. |
• According to Khosravi et al., 2025, this tension is fundamental in medical image synthesis: maximizing realism may compromise privacy, whereas overly constrained models reduce clinical utility.
• Principle: Privacy-preserving AI in healthcare must balance data utility and confidentiality, consistent with ethical and regulatory standards.
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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 of synthetic MRI as image-processing software represents a first regulatory precedent for the clinical use of AI-generated medical images.
• This approval:
• Confirms that synthetic images can meet quality and reliability standards comparable to real images
• Provides a regulatory framework for evaluating the safety, fidelity, and clinical utility of synthetic medical data
• Encourages further adoption of AI-generated datasets in research and clinical workflows under regulatory oversight
• Without such guidance, adoption of synthetic data in clinical practice would face uncertainty about validation, liability, and patient safety.
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• According to Khosravi et al., 2025, the FDA’s approval sets an important precedent that allows AI-generated images to be treated as equivalent to real data for certain clinical applications, provided they meet defined validation criteria.
• Principle: Regulatory validation of synthetic data is essential for clinical adoption, ensuring both safety and efficacy.
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| 7 |
Which strategy would best mitigate demographic bias in generative models according to the article?
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2. Applying diversity-aware training and fairness constraints |
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• Generative models can inherit biases present in training data, such as overrepresentation of certain demographic groups.
• If unaddressed, this leads to biased synthetic images, reducing clinical generalizability and fairness.
• Diversity-aware training ensures the model sees balanced samples across demographics, while fairness constraints guide the model to generate images that represent all groups proportionally.
• Other strategies (e.g., increasing majority population samples or reducing dataset size) exacerbate bias, while ignoring variation or avoiding external validation risks unreliable and inequitable outputs.
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• According to Khosravi et al., 2025, mitigating demographic bias is critical for equitable AI in healthcare.
• Principle: Fairness in generative models requires both balanced data representation and algorithmic constraints to avoid perpetuating or amplifying existing biases in clinical datasets.
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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 generate images through iterative denoising of noise inputs, learning the underlying data distribution.
• This iterative, probabilistic structure allows a single trained DDPM to handle multiple image-based tasks:
• Denoising: Remove noise from medical scans
• Inpainting: Fill in missing or corrupted regions
• Anomaly detection: Highlight deviations from normal anatomy or pathology
• Unlike some models requiring retraining for each task, DDPMs can reuse the same model for various applications, showcasing high versatility in healthcare imaging. |
• According to Khosravi et al., 2025, DDPMs’ flexible reverse diffusion process allows them to adapt to different tasks without retraining, making them valuable for multi-purpose medical image synthesis and analysis.
• Principle: Versatility in generative models is enhanced when a model can perform multiple clinically relevant tasks with the same trained weights.
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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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• AI-generated synthetic images can augment educational and research datasets by providing:
• Diverse cases, including rare pathologies
• Realistic images that reflect clinical variability
• Ethically safe alternatives to using real patient data, avoiding privacy violations
• This allows students, trainees, and researchers to practice and validate AI models without compromising patient confidentiality, improving learning and model robustness.
• It does not replace real clinical training entirely, but serves as a complementary tool.
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• According to Khosravi et al., 2025, synthetic datasets are particularly valuable in medical education and multi-center research where access to large, diverse datasets is limited.
• Principle: Synthetic medical images enable scalable, safe, and realistic training and research while respecting privacy and ethical standards. |
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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 prediction models (e.g., Framingham Risk Score) originally developed in Western populations may overestimate or underestimate risk when applied to other populations.
• Differences in baseline disease incidence, diet, lifestyle, and genetic factors affect the accuracy of predicted risk.
• Regional calibration adjusts model parameters using local epidemiological data, ensuring:
• Accurate risk estimation for the target population
• Improved clinical decision-making and preventive strategies
• Without calibration, models may misclassify patients, leading to over- or under-treatment. |
• According to Nguyen et al., 2025, East Asian populations have lower baseline ASCVD incidence compared to Western populations, so Western-based models tend to overestimate risk.
• Principle: Population-specific calibration is essential for predictive model validity and clinical applicability.
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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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• The Framingham Risk Score was developed for Western populations and tends to overestimate ASCVD risk in East Asian populations due to differences in baseline incidence, lifestyle, and genetic factors.
• The China-PAR model is calibrated using population data from multiple regions in China, which allows it to:
• Achieve better predictive validity for East Asian populations
• Reduce overestimation of risk
• Using population-specific epidemiological data is therefore crucial for the accuracy and reliability of the model.
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• According to Nguyen et al., 2025, locally developed models such as China-PAR adjust risk predictions appropriately for East Asian populations, whereas Framingham tends to overestimate risk.
• Principle: Local calibration enhances predictive accuracy of risk models across populations.
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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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• Despite differences in population age structures, Japan consistently shows low age-standardized and crude CVD mortality rates.
• This indicates that Japan likely benefits from:
• Effective preventive strategies (e.g., early detection, public health campaigns)
• Advanced healthcare infrastructure and access
• Low mortality is not solely due to population size or age distribution, but reflects systematic healthcare effectiveness.
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• According to Nguyen et al., 2025, Japan’s CVD outcomes outperform neighboring countries like China, Mongolia, and North Korea, highlighting the role of prevention, lifestyle interventions, and healthcare quality.
• Principle: Comparative epidemiological analysis can reveal the impact of healthcare systems on population-level disease outcomes.
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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 models (e.g., Framingham Risk Score) are calibrated using baseline incidence, lifestyle, and risk factor distributions from Western populations.
• Applying these coefficients directly to East Asian populations can lead to:
• Overestimation of ASCVD risk, because East Asians generally have lower baseline incidence of certain cardiovascular events.
• Potential misclassification of patients, resulting in unnecessary interventions or misinformed clinical decisions.
• This limitation underscores the importance of population-specific calibration in predictive modeling.
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• According to Nguyen et al., 2025, using uncalibrated Western coefficients in East Asian cohorts systematically inflates predicted ASCVD probabilities, reducing predictive validity.
• Principle: Population-specific calibration ensures accuracy and avoids systematic bias in risk prediction models.
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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 risk models (e.g., China-PAR, Suita Score, KRPM) are calibrated using local epidemiological data, reflecting:
• Population-specific incidence rates
• Lifestyle factors such as diet, smoking, and physical activity
• With more accurate risk prediction, policymakers can:
• Design targeted prevention programs for high-risk groups
• Allocate healthcare resources efficiently
• Implement population-level interventions tailored to local needs
• Using generic models from other regions may misdirect resources or misclassify risk, reducing effectiveness of national health policies.
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• According to Nguyen et al., 2025, country-specific calibration improves predictive validity, which enables evidence-based national prevention strategies and public health planning.
• Principle: Population-specific risk assessment informs precise, effective, and equitable health policy interventions.
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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 factors (e.g., income, education, occupation) influence health outcomes and cardiovascular risk beyond biological markers.
• Excluding these variables can lead to:
• Incomplete risk assessment, as non-biological determinants are ignored
• Potential residual confounding, reducing predictive validity for populations with diverse social conditions
• Models that include only biological variables may fail to capture the full spectrum of risk, limiting their utility for public health planning and personalized interventions.
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• According to Nguyen et al., 2025, consideration of socioeconomic determinants is important for accurate ASCVD risk prediction in heterogeneous populations.
• Principle: Comprehensive risk models integrate both biological and social determinants to enhance predictive accuracy and equity.
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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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• Traditional ASCVD risk models primarily rely on demographics, blood tests, and basic clinical variables, which may miss complex patterns.
• AI enables integration of multimodal data such as:
• Medical imaging (e.g., coronary artery scans)
• Lifestyle factors (e.g., diet, physical activity, smoking)
• Socioeconomic determinants
• This approach allows more personalized and accurate risk predictions, improves population-specific calibration, and supports preventive interventions tailored to East Asian populations.
• AI complements rather than replaces traditional models, enhancing predictive power and clinical applicability.
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• According to Nguyen et al., 2025, next-generation AI models can leverage multimodal datasets to improve ASCVD risk assessment, especially where population characteristics differ from Western cohorts.
• Principle: Integrating diverse, population-specific data enhances predictive validity and individualized care.
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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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• CVD mortality rates differ across countries due to differences in healthcare infrastructure, public health initiatives, and preventive strategies.
• South Korea has relatively lower CVD mortality, reflecting effective prevention programs, screening, and treatment strategies.
• Mongolia, with higher mortality, may have less comprehensive prevention programs and healthcare access limitations.
• Thus, comparing mortality rates can highlight the impact of national prevention policies rather than being solely due to demographic or genetic differences. |
• According to Nguyen et al., 2025, variations in age-standardized CVD mortality among East Asian countries are strongly influenced by the quality and coverage of preventive healthcare programs.
• Principle: Epidemiological differences can indicate effectiveness of national health interventions and preventive strategies.
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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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• Current models in East Asia (e.g., China-PAR, Suita, KRPM) are country-specific, limiting cross-country comparability and generalizability.
• Multinational data-sharing platforms would allow:
• Integration of large, diverse datasets across East Asian populations
• Calibration and harmonization of models for consistent risk prediction
• Identification of regional differences in lifestyle, genetics, and healthcare access
• This approach enhances model accuracy, predictive validity, and applicability for policy and clinical decision-making.
• Relying solely on Western models or ignoring variability would reduce effectiveness and misrepresent risk.
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• According to Nguyen et al., 2025, collaborative East Asian datasets are critical for developing robust, population-specific, and interoperable ASCVD risk models.
• Principle: Cross-national collaboration and data integration improve predictive models while respecting population heterogeneity.
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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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• The image generation trilemma describes a trade-off among image diversity, quality, and generation speed.
• VAEs: tend to produce diverse but lower-quality images, often blurry.
• GANs: can generate high-quality, realistic images and maintain moderate diversity, but mode collapse can reduce variety.
• DDPMs: produce high-fidelity and diverse images, but are computationally intensive and slower.
• Thus, GANs represent a compromise solution, balancing quality and diversity, but are vulnerable to mode collapse, which must be mitigated in medical imaging applications.
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Based on the figure illustrating the image generation trilemma in the article (ScienceDirect 2025, S258975002500072X):
• There are inherent trade-offs among image diversity, quality, and generation speed in generative models.
• VAEs tend to prioritize diversity but often produce lower-quality or blurry images.
• GANs provide a balance between image quality and diversity, but they can be affected by mode collapse, which reduces variety in generated images.
• DDPMs emphasize high-fidelity and realistic image generation, though they require more computation and longer generation times.
• Understanding these model-specific strengths and limitations is crucial for selecting the appropriate generative model for medical image synthesis applications.
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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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1. Ischemic heart disease (IHD) accounts for a higher proportion of CVD deaths in Japan and South Korea compared with China, suggesting regional lifestyle or prevention differences. |
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• Epidemiological data in the figure indicate that:
• Japan and South Korea have a higher proportion of IHD-related deaths.
• China has relatively more stroke-related deaths, particularly hemorrhagic stroke.
• This suggests that regional differences in diet, lifestyle, hypertension management, and preventive healthcare influence the subtype distribution of CVD.
• Understanding these patterns helps tailor public health strategies and prioritize interventions for country-specific CVD burdens.
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• Based on ScienceDirect 2025, S2772374725000511, Figure comparing CVD subtypes in East Asian countries:
• Highlights variation in IHD vs. stroke proportions among Japan, South Korea, and China.
• Principle: Regional lifestyle, diet, and healthcare policies shape CVD subtype prevalence.
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