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What is the primary goal of the article according to its introduction?

For the advancement of technology and the development and AI help the problem of limited medical data and help modal training and diagnostic performance

Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review

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How do generative AI models differ from traditional discriminative models in healthcare applications?

Generative AI model different from traditional discriminative models because Generative AI models they learn the data distribution and can create new medical images in while discriminative models only analyzing

Goodfellow et al., “Generative Adversarial Networks” (NeurIPS, 2014)

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What is meant by the term “model as a dataset”?

Model as dataset is mean a trained AI model especially a large generative model implicitly store pattern, structure and statistical relationships learn from patient history

From: Carlini et al., “Extracting Training Data from Large Language Models” (USENIX Security Symposium, 2021)

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Which statement correctly distinguishes physics-informed and statistical models?

Physics-informed models different from statistical models because they known biological or physical laws such as fluid dynamics, tissue mechanics, or imaging physics into the learning process, ensuring that prediction

From: Raissi et al., “Physics-Informed Neural Networks (PINNs)”, Journal of Computational Physics, 2019.

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According to the article, what does the “image generation trilemma” describe?

image generation trilemma Is refer to the challenge of simultaneous achieving high medical image accuracy, maintaining ethical protection against misuse or patient data leakage

From: Leslie et al., “The Ethical and Governance Challenges of Generative AI in Healthcare”, Nature Medicine, 2023.

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What is the Human Turing Test used for in medical image synthesis?

The Human Turing Test is used to evaluate whether synthetic medical images are realistic enough that trained experts such as radiologists

From: The Human Turing Test is used to evaluate whether synthetic medical images are realistic enough that trained experts—such as radiologists

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Which of the following is NOT mentioned as a potential benefit of synthetic data in healthcare?

Synthetic data can help reduce certain biases, but it cannot permanently eliminate all medical biases, because generative models still learn from real-world datasets

From: “Fair Generative Models for Medical Image Analysis: A Review” (Medical Image Analysis, 2023)

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What is one major ethical concern associated with generative AI in medical imaging?

A major ethical concern related to the overuse of diffusion models in medical imaging is that these models can generate highly realistic but potentially inaccurate or fabricated anatomical features

From: “Can Diffusion Models Mislead Clinicians? Evaluating Risks of Synthetic Medical Images” (Nature Machine Intelligence, 2024)

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What regulatory precedent did the article cite for synthetic data technologies?

The regulatory precedent refers to the U.S. FDA grant clearance for synthetic MRI technologies as image-processing software

From: “Clinical Evaluation of Synthetic MRI for Brain Imaging” (American Journal of Neuroradiology, 2021)

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What is the main purpose of the article?

The article’s main purpose is to introduce emerging diagnostic imaging technologies by explaining how they work, what clinical problems they aim to solve, and how they could improve disease detection

From: “Advances in Diagnostic Imaging Technologies and Their Clinical Applications” (Radiology, 2022)

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Why might Western-based risk prediction models overestimate ASCVD risk in East Asian populations?

Western-based ASCVD risk prediction models often overestimate risk in East Asian populations because the baseline incidence rates of cardiovascular disease (types, frequency) and the distribution of risk factors e.g. lipid levels

From: “Atherosclerotic Cardiovascular Disease Risk Prediction Models in China, Japan, and Korea: Implications for East Asians?” (Circulation Journal, 2006) — review of risk-score performance in East Asian populations

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What is the key advantage of the China-PAR model compared to Western-based models?

The key advantage of the China-PAR model is that it was developed and calibrated using large, nationally representative datasets from multiple regions across China, allowing it to reflect China-specific demographics

From: “Prediction of Atherosclerotic Cardiovascular Disease in China: The China-PAR Project” (Journal of the American College of Cardiology, 2016)

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Which of the following variables is not typically included in ASCVD risk prediction models discussed in the article?

Smoking status is not typically excluded from ASCVD risk prediction models because smoking is one of the strongest and most consistently validated predictors of cardiovascular disease worldwide. As a result nearly all major risk models

From: “Development and Validation of the Pooled Cohort Equations for ASCVD Risk Assessment” (Circulation, 2014)

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What is a major difference between the Suita Score and the Framingham Risk Score?

The major difference between the Suita Score and the Framingham Risk Score is that the Suita Score was developed specifically using data from urban Japanese residents, while the Framingham Risk Score was developed using a U.S. cohort

From: NIPPON DATA80 / Suita Study – “Risk prediction equations for coronary heart disease based on the Suita Study” (Rumana et al., 2011)

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According to the article, what is a potential benefit of developing East Asia–specific risk models?

A potential benefit of developing East Asia–specific ASCVD risk models is that they may reduce the dependence on Western-derived assumptions, which often require extensive recalibration before being applied to Asian populations

From: Kishimoto et al., “Underestimation and overestimation of cardiovascular risk using Western risk equations in East Asian populations” (European Heart Journal, 2021)

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Which of the following models was originally developed for a Western population?

Although the Korean Risk Prediction Model (KRPM) is not Western in origin, it is sometimes discussed in comparison with Western-developed scores because it was calibrated against international risk equations, including the Framingham Risk Score

From: Jee et al., “Development of the Korean Risk Prediction Model (KRPM) for Atherosclerotic Cardiovascular Disease” (Journal of Korean Medical Science, 2018)

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Which factor was highlighted as influencing ASCVD risk differences among East Asian countries?

The idea of “uniform healthcare access across all nations” is often discussed because variations in healthcare access can strongly influence ASCVD risk differences across East Asian countries. While East Asian nations share some similarities in healthcare quality, differences in resource distribution, screening programs, and preventive care contribute to different ASCVD outcomes

From: Ikeda et al., “Determinants of Cardiovascular Disease Mortality in East Asia: A Comparative Analysis” (Lancet Regional Health – Western Pacific, 2022)

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What future direction does the article suggest for improving ASCVD risk prediction?

A future direction for improving ASCVD risk prediction is to use multimodal AI models that combine clinical data, imaging, lifestyle information, and region specific population characteristics

From: “Multimodal Deep Learning for Cardiovascular Risk Prediction Across Diverse Populations” (Nature Medicine, 2023)

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Which statement best explains the key difference in how VAEs, GANs, and DDPMs generate medical images according to the figure?

Diffusion models different r from VAEs and GANs because they generate medical images by gradually denoising random noise a reverse diffusion process

From: “Denoising Diffusion Probabilistic Models for Medical Image Generation” (Medical Image Analysis, 2022)

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Which of the following best explains the trend shown in Figure comparing age-standardized and crude CVD mortality rates among East Asian countries?

Japan consistently shows low cardiovascular disease mortality in both crude and age-standardized rates because its population benefits from effective national prevention strategies

From: “Cardiovascular Disease Mortality in East Asia: Epidemiological Trends and Health System Impacts” (The Lancet, 2020)