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

As a name of the article that said that "Exploring the potential of generative artificial intelligence in medical image synthesis: opportunities, challenges, and future directions" . You can assume that the article telling about exporing advacements ,Application and Challenges of AI

Exploring the potential of generative artificial intelligence in medical image synthesis: opportunities, challenges, and future directions

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

AI normally summarize the information from all over the internet to give you an answer.

which have shown promising results in tasks such as answering medical questions, summarising medical documents, and suggesting potential differential diagnoses on the basis of patient symptoms and test results

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

It's a concept of making a summary and sharing a data by synthetic a data.

The advancement of generative artificial intelligence introduces a new concept in data sharing, which we refer to as a model as a dataset. In this concept, generative models learn and store patterns and characteristics of the original data in their internal parameters (weights).13 These trained weights contain a compressed version of the key features and relationships of the training data. Unlike traditional dataset sharing, which involves transferring actual images, sharing model weights provides an efficient alternative that allows others to generate new synthetic images with properties similar to the original data. These synthetic datasets have been shown to closely resemble the source data and capture their distribution, including the relationship of different anatomical features and their correlation with different pathological processes.

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Which statement correctly distinguishes physics-informed and statistical models?

Physics-Informed Model rely on Biological or Physical Prinnciple

Physics-informed models are primarily rule-based approaches that incorporate domain-specific knowledge and physics principles through mathematical equations and explicit constraints to generate realistic and physically plausible data. Rather than learning the patterns directly from data, these models encode expert knowledge and known physics laws (eg, fluid dynamics, tissue biomechanics, or radiation physics) to simulate biological phenomena. These models have been applied successfully in medical imaging to simulate anatomical structures (such as a shape model of the femoral bone), physiological processes (such as blood flow dynamics in vascular structures), and medical interventions (such as simulating the distribution of the radiation dose in radiotherapy planning).14 Physics-informed models offer high fidelity and interpretability but might require extensive domain expertise and computational resources. In contrast to physics-informed models, statistical models learn from data patterns and distributions (figure 1). Among them, variational autoencoders (VAEs) function by compressing data into a lower-dimensional representation, also known as latent space, and then reconstructing the data, thereby capturing the data distribution effectively.15 Generative adversarial networks (GANs) operate through a dual-network system, in which a generator creates data samples and a discriminator evaluates these data samples and provides feedback to the generator.16 This synergy continually enhances the quality and realism of the data generated. Denoising diffusion probabilistic models (DDPMs) introduce noise into an image and learn to reverse this process, producing high-quality samples.

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

As a triangle that rep

Figure 2. 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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What is the Human Turing Test used for in medical image synthesis?

Also to synthesis from the data.

Image metrics When ground truth images are available—for example, in tasks such as super resolution and denoising of medical images—traditional metrics such as structural similarity index and the peak signal-to-noise ratio can be used to measure the similarity between the generated and reference images.36,37 However, in the absence of ground truth—for example, in class-conditioned image generation—alternative metrics are required. For instance, classification accuracy score trains a classification model on derived medical data and evaluates its performance on real images, providing insights into the domain adaptation capabilities of the generation models.38 Another widely adopted metric is the inception score, which uses an inception network pretrained on ImageNet to evaluate class predictions for a set of generated samples.39 Fréchet inception distance (FID) compares the means and covariances of features extracted by an ImageNet-pretrained inception network between the generated and real samples.40 By accounting for the target distribution, FID provides a better estimate of image diversity than inception score. Several variants and improvements of FID have been proposed—eg, the kernel inception distance is a variant of FID that enables metric calculation using a small number of samples, unlike FID calculation, which requires generation of a large number of samples and is resource intensive.41 One limitation of these metrics is that they depend on pretrained networks, and unlike natural images, no universally accepted model for feature extraction exists in medical imaging.

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

Synthetic data is a summary of data not making a new data

Generative models and their synthetic datasets have numerous applications in medical imaging (panel 1). One well studied use case involves supplementing or replacing real data to train deep learning models for downstream tasks such as classification or segmentation. Generated images can be conditioned on class labels (eg, presence or absence of pneumonia) or descriptive text (eg, right middle lobe consolidation). Research has shown that images generated by GANs and DDPMs can improve the performance of downstream pathology classifiers substantially.7,19,30 Notably, the classifier performance improves as more synthetic data are added to the real dataset. In some cases, a sufficiently large pool of generated images can match the performance benefit of real data, potentially opening new avenues for data sharing whereby synthetic data acts as a replacement of the original data.8 However, when training and evaluating generative models, caution is required to avoid distribution leakage (in which a patient is represented in both training and test data), which could overestimate performance improvements.8 Of note, repeatedly training image generation models on the output of other generative models (usually more than three iterations) risks mode collapse, which degrades the quality of the final model.31

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

If they lack of synthetic image is it possible that they will it as a data and it will become an error.

One of the key advantages of generating data via statistical models lies in their ability to increase dataset size and diversity. Preliminary evidence suggests that generative models can be trained to disentangle specific associations within data, allowing for the creation of novel combinations that might not be readily available in real-world datasets.52,53 For instance, a model trained on brain MRI scans can generate images with varying degrees of atrophy or lesion load, independent of factors such as age or sex. Such disentanglement enables training models to detect specific pathologies without confounding the effects of other variables. As mentioned earlier, supplementing increased dataset size with generated images could lead to enhanced downstream model performance.8 Moreover, targeted oversampling of minoritised sociodemographic groups or patients diagnosed with rare diseases through synthetic data generation has been shown to close the fairness gap by 40%.22 Synthetic data generation closes this fairness gap by facilitating an increase in dataset sizes that represent the original dataset distribution for various subgroups. Privacy preservation Synthetic datasets offer a privacy-preserving solution to the challenges of sharing and utilisation of data in medical research.54 Generative artificial intelligence anonymises sensitive patient information by generating realistic images that mimic biological characteristics of real patient data (both visually and in the model feature space) without direct replication of original data.55 Such anonymisation enables the creation of datasets that can be shared and analysed without compromising patient privacy, which further opens up new avenues for collaborative research and facilitates the development of robust, privacy-compliant artificial intelligence models in medical imaging.

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

As the article said "Subtle Medical’s SubtleHDTM wins FDA clearance, setting a new benchmark for MRI image quality and speed".

Subtle Medical’s SubtleHDTM wins FDA clearance, setting a new benchmark for MRI image quality and speed

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What is the main purpose of the article?

As a title of the article.

Atherosclerotic Cardiovascular Disease Risk Prediction Models in China, Japan, and Korea: Implications for East Asians?

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

as the sentence said "Declining fertility rates and increasing life expectancies will ensure that ASCVD continues to be one of the most common noncommunicable, chronic diseases affecting East Asian persons for future decades".

East Asian people make up 20.7% of the world’s population based on the latest estimates by the United Nations.1 As the fastest growing immigrant population in the United States, Asians comprise approximately 7% of the U.S. population, with East Asians making up the largest Asian subgroup (eg, ∼40%). In 2019, cardiovascular disease (CVD) claimed 5.2 million lives in East Asian countries.2 Atherosclerotic cardiovascular disease (ASCVD), including ischemic heart disease, ischemic stroke, and peripheral arterial disease, was the leading cause of cardiovascular morbidity and mortality among East Asian persons.2 Declining fertility rates and increasing life expectancies will ensure that ASCVD continues to be one of the most common noncommunicable, chronic diseases affecting East Asian persons for future decades

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

As a key word of "China-PAR".

ASCVD risk prediction in China Several risk scores have been evaluated for ASCVD risk prediction in China. In 2003, the CMCS (Chinese Multi-provincial Cohort Study) of 30,121 adults (ages 35 to 64 years) evaluated the performance of the Framingham CHD risk score with equations derived from CMCS.25 The investigators found that when compared directly and after recalibration, the original Framingham equation significantly overestimated absolute CHD risk in the CMCS cohort, which was mainly driven by differences in the mean CHD risk and the levels of major risk factors between the 2 cohorts. Specifically, the 10-year CHD event rates were 8.0% and 2.8% in Framingham men and women, respectively, compared with 1.5% and 0.6% in the CMCS men and women. With respect to cardiac risk factors, men and women in the Framingham cohort had higher TC and lower HDL-C, respectively, than those in the CMCS cohort. However, men in the Framingham cohort had lower and women had higher smoking rates in comparison with men and women in the CMCS cohort. Importantly, a recalibration of the Framingham Risk Score (FRS) equation (ie, the mean risk of CHD and mean levels of risk factors derived from Framingham cohort), performed by replacing the corresponding estimations from CMCS cohort, substantially improved the accuracy of risk prediction.25 Even after recalibrating Framingham risk calculators, the China-MUCA (Multicenter Collaborative Study of Cardiovascular Epidemiology) cohort study reported that CHD risk for both men and women was still substantially overestimated.26 More recently, another large cohort study, China-PAR (Prediction for ASCVD Risk in China) project, found that the PCE had low discrimination ability and poor calibration for Chinese men.27 These findings highlighted the importance of developing CVD risk prediction models based on data from China cohort studies. Based on data from the CMCS cohort study, the first sex-specific ASCVD risk prediction equations and stratification algorithms were published in 2003 and subsequently updated in 2018.28,29 The China-MUCA study and China-Par project developed and published risk predictive models to estimate 10-year risk ASCVD in Chinese people.26,27 A comparison of these risk prediction models is shown in Table 1. After the development of 10-year ASCVD risk equations from large, long-term cohort studies, CMCS and China-PAR cohort studies were utilized to create lifetime ASCVD risk prediction models for young and middle-aged people.30,31 The lifetime ASCVD risk prediction tool can identify those with lower 10-year ASCVD risk but higher lifetime risk, thereby, facilitating earlier prevention intervention, including motivating lifestyle modifications for these individuals. All of these studies developed either categorized algorithms of risk classification by flowcharts, risk scoring systems, or web-based risk calculators to facilitate the application of risk assessment into clinical practice and for patient education.26, 27, 28

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

From reading in Table 1. ASCVD Risk Assessment Tools in China.

Table 1. ASCVD Risk Assessment Tools in China

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

ASCVD risk prediction in Japan Since 1960, when the first epidemiologic studies of CVD were performed in Japan, high stroke mortality rates but low CHD mortality rates were observed.46 Although stroke mortality has significantly improved in subsequent decades, CHD mortality remains low compared with Western populations. The 2012 Japan Atherosclerosis Society (JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases was the first guideline to introduce absolute risk assessment of ASCVD and used the NIPPON DATA80 risk chart to predict 10-year CHD mortality.44 Similarly, the 2019 Japanese Society of Hypertension incorporated absolute risk assessment as a reference to inform blood pressure treatment thresholds and management.45 In the 2017 JAS guideline,46 the Suita score was able to accurately estimate the absolute incidence of CHD by incorporating demographics and risk factors including age, sex, smoking, blood pressure level, HDL-C, LDL-C, impaired glucose tolerance, and family history of premature CHD. The Suita score was chosen from 10 different published risk prediction scores in Japan where internal validation was carefully performed. Although attempts were made to validate the predictive model for the guidelines in an external population, even within Japan, cardiovascular disease risk differs between urban and nonurban areas. Thus, it was extremely difficult to set a standard population for external validation.47 Based on the algorithm for ASCVD risk assessment, individuals are first screened to determine if they should be categorized as primary or secondary prevention candidates. They are also evaluated for high-risk comorbidities including diabetes, chronic kidney disease, noncardiogenic stroke, and peripheral artery disease. If none of these conditions are present, the Suita score is calculated and individuals are stratified into low-, moderate-, and high-risk categories. The LDL-C targets in primary prevention are set at <160 mg/dL for low risk, <140 mg/dL for moderate risk, and <120 mg/dL for high risk, respectively. The LDL-C target for patients with established CHD is <100 mg/dL, but if they also have a history of familial hypercholesterolemia or acute coronary syndrome, then a lower target LDL-C level of <70 mg/dL should be considered. Patients with both diabetes and ASCVD should also be treated to an aggressive LDL-C goal of <70 mg/dL. It is important to note that the absolute ASCVD risk estimated by the Suita score only includes CHD and not stroke, unlike the PCE and SCORE2 risk calculators that include both. In Japan, however, cerebral hemorrhage accounts for a high proportion of strokes, whereas the percentages of lacunar, cardioembolic, and atherothrombotic strokes are almost equivalent,48,49 with the first 2 not being associated with hypercholesterolemia.48 Although Japanese people have a high incidence of stroke, they have a relatively low proportion of stroke phenotypes associated with dyslipidemia, and therefore it has been difficult to use “stroke” when setting lipid management targets. In contrast, hypercholesterolemia is a strong risk factor for atherothrombotic infarction. Accordingly, the new 2022 JAS guideline, uses a recently published risk score from the Hisayama study that predicts incidence of the combined outcome of CHD and atherothrombotic cerebral infarction for individual risk assessment.50 A summary of ASCVD calculators used in Japan is shown in Table 2.

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

The most recently developed Korean ASCVD prediction model is based on data from over 150,000 participants in the Korean Genome and Epidemiology Study. The risk predictors are similar to previous models, but this model includes large-scale prospective cohort data and was validated both internally and externally. In addition, the new model may have better clinical utility because the incidence of CVD in Koreans is rapidly increasing. Although ASCVD risk scores are not officially adopted in clinical practice guidelines, many CVD risk prediction studies have been published in Korea utilizing new biomarkers and imaging modalities.75, 76, 77, 78, 79, 80, 81 Several of them have demonstrated improvements in ASCVD predictive power using CAC or CTA and employing machine learning methods. Earlier studies cross-sectionally compared CAC and Western-derived ASCVD risk scores among asymptomatic individuals and reported that high CAC levels are also observed among some Korean persons with low to moderate ASCVD risk.75,77 These findings indirectly support the usefulness of CAC-based risk classification. Later studies added more direct evidence of CAC score in the risk classification of asymptomatic Korean adults. The CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes InteRnational Multicenter) study evaluated the clinical utility of CAC and coronary CTA in Korean as well as European/American populations.74 The CONFIRM study reported that CAC improved risk stratification and provided incremental value beyond FRS for predicting major adverse cardiac events.74 Another Korean study reported that the addition of CAC to FRS improved risk prediction for CVD mortality in young adults but not in older individuals.82 The predictive value of coronary CTA was also evaluated in combination with CAC. In a 2-year follow-up study of Koreans participating in health screens, both CAC and coronary artery stenosis on coronary CTA are independent predictors of CVD outcomes. However, when CAC and coronary CTA stenosis were evaluated simultaneously, CVD risk was associated only with stenosis on coronary CTA but not with CACS.76 In the CONFIRM study, coronary CTA provides incremental predictive value for asymptomatic individuals with moderate CAC scores (100-400), but not for lower or higher CAC scores.78 There are also studies that evaluated the usefulness of fundoscopy.80,81 A Korean study found that virtual assessment of CAC estimated from deep learning analysis of retinal photographs is comparable to CT-measured CAC in predicting CVD events and improves current risk stratification for cardiovascular events.81 If further validated, fundoscopy plus deep learning algorithms have the potential to serve as a cost-effective and radiation-free alternative to measuring CAC, particularly in resource-limited settings. Collectively, these studies suggest that the inclusion of imaging strategies to improve the accuracy of risk calculators such as SCORE2 and SCORE2-OP in young and old Koreans, respectively

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

Importantly, a recalibration of the Framingham Risk Score (FRS) equation (ie, the mean risk of CHD and mean levels of risk factors derived from Framingham cohort), performed by replacing the corresponding estimations from CMCS cohort, substantially improved the accuracy of risk prediction.25 Even after recalibrating Framingham risk calculators, the China-MUCA (Multicenter Collaborative Study of Cardiovascular Epidemiology) cohort study reported that CHD risk for both men and women was still substantially overestimated

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

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

Future Directions and Conclusions In this overview of ASCVD risk assessment in East Asian countries, specifically China, Japan, and South Korea, ASCVD risk is significantly overestimated, in particular CHD, when applying calculators developed in the United States including the FRS and PCE. Unlike Europe and the United States, incidence of CHD is much lower while stroke rates are higher in Japan, Korea, and China. Studies to recalibrate these risk scores have been unsatisfactory, resulting in each country developing their own risk prediction scores based on epidemiologic studies using native cohorts and traditional risk factors.29,84,85 Unfortunately, many of these national risk prediction scores lack external validation and generalizability. Furthermore, there are challenges to implementation and adherence of guidelines in clinical practice. Application of additional risk enhancers such as CAC score and biomarkers have not been extensively studied to determine their ability to improve risk prediction models. Moreover, the utility of using specific biomarkers in risk prediction algorithms may be limited if they are not routinely measured in clinical practice. Each country's risk score was developed using Western risk calculators as a framework, and no cross validation has been performed between among China, Japan, and South Korea, nor among a larger aggregate that includes other East Asian countries. This may provide an opportunity to develop a more refined regional risk score and new risk indices through collaboration given similarities in ASCVD prevalence, disease characteristics, and lifestyles.

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

As In contrast to physics-informed models, statistical models learn from data patterns and distributions (figure 1). Among them, variational autoencoders (VAEs) function by compressing data into a lower-dimensional representation, also known as latent space, and then reconstructing the data, thereby capturing the data distribution effectively.15 Generative adversarial networks (GANs) operate through a dual-network system, in which a generator creates data samples and a discriminator evaluates these data samples and provides feedback to the generator.16 This synergy continually enhances the quality and realism of the data generated. Denoising diffusion probabilistic models (DDPMs) introduce noise into an image and learn to reverse this process, producing high-quality samples.17

In contrast to physics-informed models, statistical models learn from data patterns and distributions (figure 1). Among them, variational autoencoders (VAEs) function by compressing data into a lower-dimensional representation, also known as latent space, and then reconstructing the data, thereby capturing the data distribution effectively.15 Generative adversarial networks (GANs) operate through a dual-network system, in which a generator creates data samples and a discriminator evaluates these data samples and provides feedback to the generator.16 This synergy continually enhances the quality and realism of the data generated. Denoising diffusion probabilistic models (DDPMs) introduce noise into an image and learn to reverse this process, producing high-quality samples.17

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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?

Epidemiology of ASCVD in East Asian populations living in Asia and in the United States An aging population together with the exponential rise in obesity and diabetes will continue to fuel the higher incidence of ASCVD among East Asian Americans and East Asian natives. Below, we summarize and compare the existing data on the epidemiology of ASCVD in East Asian natives and East Asian Americans. It is well established that East Asian countries have a specific epidemiological pattern of CVD. In 2019, nearly 5.2 million East Asian natives died of CVD. Overall crude CVD mortality in East Asia was 349 of 100,000 with notable differences in mortality rates across East Asian countries (Figure 1).2 Of the 5 major countries of this region, South Korea had the lowest crude CVD mortality rate (145 of 100,000) while North Korea had the highest (391 of 100,000). In 2019, ischemic heart disease and ischemic/hemorrhagic stroke accounted for approximately 87% of all CVD deaths in East Asia with stroke making up more than one-half of them (Figure 2).2 Of total deaths in East Asian countries, Japan had the lowest proportion of stroke deaths (39%), while China (48%) and South Korea (47%) had comparable rates. Although hemorrhagic strokes appeared to be slightly more frequent in East Asia overall, the proportion of hemorrhagic strokes varied significantly across the region. On average, 52% of stroke deaths were attributed to hemorrhage stroke, ranging from as low as 36% in Japan to as high as 50% in China.2 Although peripheral vascular disease continues to be an important contributor to CVD, the availability of peripheral vascular disease mortality data in East Asian countries remains limited.