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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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By the article said that 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. |
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 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 Model rely on Biological or Physical Prinnciple |
Two broad categories of generative models provide the ability to generate synthetic datasets: physics-informed and statistical models.
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.17
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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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It a generate image |
Statistical models encounter the generative artificial intelligence trilemma, which involves balancing high sample quality, comprehensive mode coverage, and rapid sampling rates (figure 2).18 VAEs are notable for their quick sampling capabilities, sometimes resulting in lower sample quality. GANs excel at generating high-quality samples but might not always capture all data variations, leading to low mode coverage, known as mode collapse. DDPMs stand out for their ability to generate samples of exceptional quality and extensive mode coverage, albeit at a slower sampling rate. End users select the generative model that matches their application of interest, balancing the desired image quality and speed. For dataset generation purposes, the priority typically shifts towards ensuring high image quality and comprehensive mode coverage, often outweighing concerns of sampling speed. |
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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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As a meaning Of FID: compares means and covariances of features extracted from generated and target distributions using an inception network pretrained on ImageNet and
SSIM: assesses structural similarity between generated and reference images by considering luminance, contrast, and structure |
FID: compares means and covariances of features extracted from generated and target distributions using an inception network pretrained on ImageNet
SSIM: assesses structural similarity between generated and reference images by considering luminance, contrast, and structure
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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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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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Why is the FDA’s approval of synthetic MRI technology significant for future AI-generated data?
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It establishes a framework for validating synthetic data equivalence in clinical use. |
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Subtle Medical
Subtle Medical’s SubtleHDTM wins FDA clearance, setting a new benchmark for MRI image quality and speed |
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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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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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Denoising diffusion probabilistic models (DDPMs) introduce noise into an image and learn to reverse this process, producing high-quality samples.
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.
DDPMs=denoising diffusion probabilistic models.
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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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Increased dataset size and diversity
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. |
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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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However, they have only been assessed at the individual level, and without validation in population-based cohorts, are difficult to incorporate into risk prediction models. They have limited clinical application since they are not routinely measured at clinic visits. |
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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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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. |
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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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Rely from the article "total deaths in East Asian countries, Japan had the lowest proportion of stroke deaths (39%), " . |
total deaths in East Asian countries, Japan had the lowest proportion of stroke deaths (39%), |
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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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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. |
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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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Although country-specific risk assessment tools are readily available in China, significant challenges remain. Most of the cohort studies collected baseline information in the 1990s, but there have been changes in risk factor prevalence among more contemporary target populations. Risk prediction models need to be recalibrated, and some studies have started to evaluate these older equations with data collected from large contemporary cohorts. |
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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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From altogether arguing their effects may be already reflected in socioeconomic data |
altogether arguing their effects may be already reflected in socioeconomic data |
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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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BK and BJE have pending patents on radiographic image generation and feature extraction from generative models (63/583044 and PCT/US2023/074166). JWG is a member of the American College of Radiology Artificial Intelligence Advisory Group, the Society of Imaging Informatics in Medicine Board, and the Health Level 7 Standards Board; has received honoraria from the National Bureau of Economic Research for writing in their 2023 conference book; and has grants or contracts from Lunit for artificial intelligence evaluation for digital breast tomosynthesis evaluation, Clarity consortium for breast artificial intelligence, and DeepLook for artificial intelligence validation. All other authors declare no competing interests. |
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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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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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they may not be readily available, established risk modifiers such as subclinical atherosclerosis detected by noninvasive imaging such as CAC, carotid ultrasound, and ABI should be studied more extensively in East Asian countries and may improve risk refinement with the caveat that many patients receiving these imaging studies may be at higher risk for ASCVD, which results in selection bias and may affect the accuracy of risk assessment. |
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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 between image quality and diversity but may suffer from mode collapse. |
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As A Keyword GANs Proportion of Subtypes of CVD in Total CVD Death
Percentage of total cardiovascular disease (CVD) deaths attributable to ischemic heart disease, stroke, and other CVDs in China, Japan, and South Korea, and all of East Asia. The proportion of deaths caused by stroke are further stratified into the percentage of total stroke deaths caused by ischemic or hemorrhagic stroke. Data were obtained from the open database of the Global Burden of Disease Study in the Global Health Data Exchange. |
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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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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As an article said Proportion of Subtypes of CVD in Total CVD Death
Percentage of total cardiovascular disease (CVD) deaths attributable to ischemic heart disease, stroke, and other CVDs in China, Japan, and South Korea, and all of East Asia. The proportion of deaths caused by stroke are further stratified into the percentage of total stroke deaths caused by ischemic or hemorrhagic stroke. Data were obtained from the open database of the Global Burden of Disease Study in the Global Health Data Exchange. |
Proportion of Subtypes of CVD in Total CVD Death
Percentage of total cardiovascular disease (CVD) deaths attributable to ischemic heart disease, stroke, and other CVDs in China, Japan, and South Korea, and all of East Asia. The proportion of deaths caused by stroke are further stratified into the percentage of total stroke deaths caused by ischemic or hemorrhagic stroke. Data were obtained from the open database of the Global Burden of Disease Study in the Global Health Data Exchange. |
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