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

I chose this because in the articl, it introduce the revalenace of AI and mental health and how their could be possible gaps that AI could come and help It then discusses how AI can help with diagnosis, therapy support, and patient monitoring. It also talks about the challenges in the future

Paragraph 4 "In choosing a narrative review format, this article does not aim to exhaustively catalog every study or quantitative result. Instead, it synthesizes themes and developments from the current literature to provide a broad yet insightful picture of the AI-mental health intersection."

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

Because generative models is a creative tools creates something new , it learns what real data looks and make creative things that looks real, such as new images, fake. In this health care field, they can make medical images, paitents notes etc. However, model the discrimative only classity based on information that is given and the data must be existing.

This is supported by probabilistic modeling theory in machine learning. Generative models learn the full data distribution (P(X)), which allows them to create new realistic data samples. Discriminative models focus on predicting levels. because generative models are designed to learn the underlying data distrubution and create new samples that look like the original data. They do more than just label or interpret information.

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

I chose this because a model being as a data in the artciel refers to the idea of treating a trained model as a data artifact that can be shared without exposing raw data so that their privacy of paitents dont need to be shared.

Based on privacy-preserving data-sharing literature, a trained model can act as a proxy for raw data because it codes useful patterns without exposing records. (Federated Learning (Google, 2016))

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Which statement correctly distinguishes physics-informed and statistical models?

physics-informed and statistical models are approaches used in medical image analysis. Physics-informed models use real biological or physical rules to help guide how the model learns. These rules built instructions, so the model doesn’t rely only on data. Statistical models, learn from patterns in data. They do not include special scientific rules.

Inductive bias, supports this explanation because it shows why adding scientific rules improves a model.

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

image generation trilemma” refers to balancing three competing factors for image generation systems: diversity (variety of outputs), quality and speed . However, trilemma it could be a trade off since you cant make all three factors work together, so therefore, it will chose what matter the most in image gerneration

No Free Lunch Theorem- "states that no single model can perform best in all situations or across all objectives." This supoprts the trade off answer because a system working for speed won’t also be optimal for quality and diversity.

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

I chose this because Human Turing Test in medical image synthesis s used to check if experts can tell the difference between real medical images and AI-generated ones. If the experts cannot tell which images are fake, the generated images are considered realistic. This follows the original idea of the Turing Test, where a machine is judged based on whether humans think is it real or not.

Perceptual Realism Theory, say, that something is considered real if people think it as real. In the Human Turing Test for medical image synthesis, experts judge whether the AI-generated images look real.

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

Most discussions of synthetic healthcare data focus on improving diversity, preserving privacy, enabling education, and enabling multi-center collaborations. which in the article they didnt mention the permently taking out the bias as though it could to an extent.

Algorithmic Bias Theory, AI systems can inherit bias from the data they are trained on. Since synthetic data is generated from real data, it can still contain hidden biases, so bias cannot be completely eliminated.

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

One major ethical problem with generative AI in medical imaging is that it might accidentally copy real patient data. If this happens, someone could possibly identify the patient from the generated images, which creates privacy issue.

Data Reconstruction Attacks Research, can unintentionally reveal parts of their training data, meaning private patient information could be reidentified from synthetic outputs if privacy safeguards are not used.

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

The article explains that there are official regulations that allow synthetic data and AI imaging tools to be used in hospitals. One clear example is FDA approval of image-processing software, which shows that this type of AI can receive many approval.

Regulatory Approval Framework, Medical technologies must follow official government rules before being used in hospitals. FDA approval shows that AI imaging tools can pass safety

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What is the main purpose of the article?

the article focuses on developing a model that can predict cardiovascular risk across different Western populations. The article aims to create a standardized risk model that can be applied aruond the world.

Risk prediction model theory explains how researchers develop statistical models to estimate the probability of disease in different populations. Since the article focuses on creating a universal ASCVD model that can be applied across Western countries it similarly show aims to standardize and generalize cardiovascular risk prediction for many populations.

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

The Framingham Risk Score was developed from the Framingham Heart Study in the United States, which is a Western population. The other models were developed using asian populations.

Risk prediction models are developed based on data from specific populations. Since the Framingham Risk Score was created using data from a U.S. (Western) population, it is showed as a Western developed model.

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

I chose this because western-based risk prediction models are developed using Western populations, where the baseline rate of ASCVD is generally higher. If these models are applied to East Asian populations, it is likely to have lower baseline incidence rates.

(Risk Model Calibration Principle, states how models must be calibrated first. If a model developed in a high-risk population is applied to a lower-risk population without adjustment, it can lead to overestimate risk and errors.

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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 Chinese from different regions. This makes it more accurate for predicting ASCVD risk in the Chinese population compared to Westernbased models

Risk prediction models perform best when they are developed and calibrated using data from the target population. Using nationally representative data improves both accuracy and generalizability.

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

Most ASCVD risk prediction models have risk factors like age, blood pressure. But Genetic ancetsry markers are not usually part of standard risk calculators discussed in the article, it only focus on easily measurable clinical variables.

ASCVD risk models are generally based on established epidemiologic risk factors identified through long-term studies. The priportiies measureble variables rather then genetic ancestry.

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

The difference is that the Suita Score was developed using Japanese population data, making it more accurate for predicting the disease risk in Japan. In contrast, the Framingham Risk Score was developed using data from a U.S. (Western) population.

Risk prediction models perform best when developed using data from the same population in which they are applied. (Population-Specific Risk Modeling Principle)

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

Risk models made in Western countries may not work perfectly for East Asian populations because disease rates and lifestyles are different. If we create models using East Asian data, the results will be more accurate.

Risk prediction models work best when they are built using data from the same group of people they are used on. If a model is used on a different population without adjustment, it can give wrong or exaggerated risk results.

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

Different East Asian countries have different diets and lifestyles, These differences can affect blood pressure and heart disease risk, which leads to differences in ASCVD rates.

Disease risk is influenced by environmental and lifestyle factors such as diet, behavior, and cultural habits. Variations in these factors across regions can lead to differences in disease cases (Epidemiological Risk Factor Theory)

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

The article suggests improving ASCVD prediction by combining different types of data like clinians, regional inforation context. This approach can make risk prediction more personalized and accurate.

Combining multiple data sources improves prediction performance because it knows more aspects of disease risk. from the Multimodal Learning and Precision Medicine Theory

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

From the figure, VAEs first shrink the image into a smaller hidden box and then rebuild it. while GANs have two parts: one part makes fake images, and the other part checks if the images are real or fake. and DDPMs work differently, they first add noise to an image step by step, then slowly remove the noise to create a clear image.

DDPMs are based on a probabilistic diffusion process. They model image generation as a stepwise noise removal process using reverse Markov chains, which is fundamentally different from VAE and GANs.

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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?

The figure, Japan has low death rates in both types of measurement. This means that even when we adjust for age differences between countries, Japan still has fewer deaths from CVD. This suggests that Japan has good prevention, treatment, and healthcare systems make mortility rates low.

Age-standardized rates remove the effect of different age structures between countries. If a country has low mortality in both crude and age-standardized rates, it suggests true lower disease risk and effective healthcare,