Generative adversarial networks (GANs) have recently been successfully used to create realistic synthetic microscopy cell images in 2D and predict intermediate cell stages. In the current paper we highlight that GANs can not only be used for creating synthetic cell images optimized for different fluorescent molecular labels, but that by using GANs for augmentation of training data involving scaling or other transformations the inherent length scale of biological structures is retained. In addition, GANs make it possible to create synthetic cells with specific shape features, which can be used, for example, to validate different methods for feature extraction. Here, we apply GANs to create 2D distributions of fluorescent markers for F-actin in the cell cortex of Dictyostelium cells (ABD), a membrane receptor (cAR1), and a cortex-membrane linker protein (TalA). The recent more widespread use of 3D lightsheet microscopy, where obtaining sufficient training data is considerably more difficult than in 2D, creates significant demand for novel approaches to data augmentation. We show that it is possible to directly generate synthetic 3D cell images using GANs, but limitations are excessive training times, dependence on high-quality segmentations of 3D images, and that the number of z-slices cannot be freely adjusted without retraining the network. We demonstrate that in the case of molecular labels that are highly correlated with cell shape, like F-actin in our example, 2D GANs can be used efficiently to create pseudo-3D synthetic cell data from individually generated 2D slices. Because high quality segmented 2D cell data are more readily available, this is an attractive alternative to using less efficient 3D networks.

Generative Adversarial Networks Market Outlook

According to our latest research, the global Generative Adversarial Networks (GANs) market size reached $2.19 billion in 2024, reflecting the rapid adoption of deep learning technologies across industries. The market is registering a robust CAGR of 31.5% and is forecasted to achieve a value of $21.47 billion by 2033. This exponential growth is attributed to the increasing demand for advanced AI-driven content creation, synthetic data generation, and the transformative impact of GANs in sectors such as healthcare, media, and cybersecurity. The expanding ecosystem of AI research and the proliferation of high-performance computing infrastructure are further accelerating the adoption of GANs worldwide.

A primary growth factor for the Generative Adversarial Networks market is the surging need for synthetic data generation. As organizations increasingly rely on data-intensive machine learning models, GANs have emerged as a pivotal technology to generate realistic, high-quality synthetic datasets that overcome data privacy and scarcity challenges. This is particularly crucial in sectors such as healthcare and finance, where access to diverse, high-fidelity data is often restricted by regulatory requirements. GANs enable the creation of anonymized yet statistically accurate datasets, facilitating model training without compromising sensitive information. Additionally, the growing sophistication of GAN architectures has led to improved output quality, making them indispensable for simulations, product development, and algorithm validation.

Another significant driver is the integration of GANs into creative and media industries for content generation. GANs are revolutionizing image and video production by automating the creation of hyper-realistic visuals, deepfakes, and special effects, reducing both time and cost. Companies in advertising, gaming, and entertainment leverage GANs to generate novel content, restore old media, and personalize user experiences at scale. With the rise of virtual influencers, digital avatars, and immersive experiences in the metaverse, GANs are becoming foundational tools for brands seeking innovative ways to engage audiences. The continuous advancements in neural network architectures and training algorithms are further enhancing the capabilities of GANs in these creative domains.

The increasing application of GANs in scientific research and drug discovery is also fueling market expansion. In the pharmaceutical industry, GANs are utilized to design new molecular structures, predict drug efficacy, and optimize clinical trials by generating synthetic patient data. This accelerates the drug development pipeline and reduces R&D costs. Similarly, in cybersecurity, GANs are deployed to simulate cyberattacks and generate adversarial examples, helping organizations bolster their defense mechanisms. The versatility of GANs in addressing complex, real-world problems across diverse sectors underscores their growing importance and widespread adoption in the coming years.

Regionally, North America continues to dominate the Generative Adversarial Networks market, driven by a robust AI research ecosystem, significant investments from tech giants, and the early adoption of advanced machine learning solutions. However, Asia Pacific is witnessing the fastest growth, propelled by increasing government initiatives, a burgeoning startup landscape, and rapid digital transformation in countries like China, Japan, and South Korea. Europe is also making significant strides, particularly in regulated industries such as healthcare and automotive, where GANs are being leveraged for innovation and compliance. The global expansion of cloud infrastructure and cross-border collaborations in AI research are further contributing to the widespread adoption and growth of the GANs market across all regions.

Component Analysis

The Generative Adversarial Networks market is segmented by component into Software, Hardware, and Services</b&g

Generative Adversarial Networks (GANs) Market Outlook

According to our latest research, the global Generative Adversarial Networks (GANs) market size in 2024 stands at USD 2.78 billion, with robust growth expected over the next decade. The market is projected to expand at a CAGR of 32.5% from 2025 to 2033, reaching an estimated value of USD 33.5 billion by the end of the forecast period. The remarkable growth trajectory can be attributed to the increasing adoption of advanced deep learning techniques, rising demand for synthetic data generation, and the proliferation of AI-driven solutions across diverse industries. As per our latest research, key growth drivers include technological advancements, expanding applications across verticals, and the accelerating pace of digital transformation globally.

One of the primary growth factors fueling the Generative Adversarial Networks market is the surge in demand for high-quality synthetic data, which has become critical for training robust machine learning models. With the growing concerns around data privacy and the scarcity of labeled datasets, organizations are leveraging GANs to generate realistic synthetic datasets that preserve privacy while maintaining statistical validity. This trend is especially pronounced in sectors such as healthcare, where patient data sensitivity is paramount, and in the automotive industry, where synthetic data aids in developing autonomous vehicle algorithms. The ability of GANs to produce lifelike images, videos, and text data is revolutionizing data augmentation processes, reducing dependency on costly and time-consuming data collection efforts, and accelerating the pace of innovation.

Another significant driver is the rapid evolution of GAN architectures and their expanding application scope. Recent advancements in GAN technology, including StyleGAN, CycleGAN, and BigGAN, have dramatically improved the fidelity and versatility of generated content. These innovations have unlocked new possibilities in fields such as image generation, video synthesis, drug discovery, and even text-to-image synthesis. Enterprises in media and entertainment are utilizing GANs to create photorealistic visual effects, while pharmaceutical companies are leveraging the technology for accelerated drug molecule design and discovery. The versatility of GANs, coupled with their ability to automate creative processes and generate novel content, is attracting substantial investments from both established players and startups, further propelling market growth.

The increasing integration of GANs into cloud-based platforms and AI-as-a-Service offerings is also playing a crucial role in market expansion. Cloud deployment models enable organizations of all sizes to access powerful GAN capabilities without substantial upfront infrastructure investments. This democratization of access is particularly beneficial for small and medium enterprises (SMEs), allowing them to harness advanced generative AI for various applications, from marketing and e-commerce to cybersecurity and fraud detection. The scalability and flexibility offered by cloud-based GAN solutions are fostering widespread adoption, while ongoing advancements in hardware accelerators and optimized software frameworks are further lowering barriers to entry.

From a regional perspective, North America currently dominates the GANs market, driven by the presence of leading technology companies, extensive research and development activities, and a mature ecosystem for AI adoption. However, Asia Pacific is emerging as a high-growth region, fueled by rapid digitalization, increasing investments in AI research, and the proliferation of innovative startups. Europe, with its strong emphasis on data privacy and regulatory compliance, is witnessing growing adoption of GANs for privacy-preserving data generation and synthetic data applications. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by government initiatives, expanding digital infrastructure, and rising awareness about the transformative potential of generative AI technologies.

Component Analysis

The Generative Adversarial Networks market by component is segmented into software, hardware, and services. The software segment currently holds the largest share, driven by the rapid evolution of GAN frameworks, toolkits, and libraries that facilitate the development, training, and deployment of generative models. Op

A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data

The proposed work aims to create a dataset linked to the Internet of Medical Things (IoMT) context by exploiting an innovative approach to create synthetic dataset by using Generative Adversarial Networks (GANs). In particular, the synthetic dataset is created by using GANs network starting from data retrieved by the IoT sensors.

Getting Started

In order to user the dataset, simply download the repository and start to work with the xlsx files. More information are available at the following page: Vaccari, I.; Orani, V.; Paglialonga, A.; Cambiaso, E.; Mongelli, M. A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data. Sensors 2021, 21, 3726.

Please if you use this dataset in a research work, please cite this article.

IoMT devices adopted

The devices adopted to retrieve patients data are: * An electrocardiogram (ECG) patch also providing day/night movement monitoring, * A pulse meter providing oximetry monitoring, * A weight scale, * A sphygmomanometer for blood pressure monitoring, * A spirometer for peak flow and FEV1 parameters.

Data are collected every day for three consecutive months: oxygen, body temperature, heart rate, heart rate master, weight, Body Mass Index, FEV1, PEF, MAP, diastolic blood pressure, systolic blood pressure.

Repository organization

The repository is composed by 3 folder:

• Data
  • Input
    • dataPneulytics.xlsx
  • Output
    • GenData{1-11}.xlsx
• summary.xlsx

In the Data folder, two folders are presented: in the Input folder, the data retrieved by the IoMT sensors are reported while in the Output folder, the synthetic dataset generated through the GANs is uploaded. In the summary.xslx, results about comparison between the synthetic and real dataset are reported in terms of Jensen–Shannon (JS) divergence, Fréchet Inception Distance (FID), accuracy and F1 score.

Authors

• Ivan Vaccari - Concept, implementation, elaboration, paper writer - Profile
• Vanessa Orani - implementation, elaboration, - Profile
• Alessia Paglialonga - Supervisor, paper reviewer - Profile
• Enrico Cambiaso - Supervisor, elaboration, paper collaboration - Profile
• Maurizio Mongelli - Machine learning support and contribution - Profile

Synthetic Data as a Service Market Outlook

According to our latest research, the global synthetic data as a service market size reached USD 475 million in 2024, reflecting robust adoption across industries focused on data-driven innovation and privacy compliance. The market is growing at a remarkable CAGR of 37.2% and is projected to reach USD 6.26 billion by 2033. This accelerated expansion is primarily driven by the rising demand for privacy-preserving data solutions, the proliferation of artificial intelligence and machine learning applications, and stringent regulatory requirements around data security and compliance.

A key growth factor for the synthetic data as a service market is the increasing prioritization of data privacy and regulatory compliance across industries. Organizations are facing mounting pressure to comply with frameworks such as GDPR, CCPA, and other regional data protection laws, which significantly restrict the use of real customer data for analytics, AI training, and testing. Synthetic data offers a compelling solution by providing statistically similar, yet entirely artificial datasets that eliminate the risk of exposing sensitive information. This capability not only supports organizations in maintaining compliance but also accelerates innovation by facilitating unrestricted data sharing and collaboration across teams and partners. As privacy regulations become more stringent worldwide, the demand for synthetic data as a service is expected to surge, particularly in sectors such as healthcare, finance, and government.

Another significant driver is the rapid adoption of artificial intelligence and machine learning across diverse sectors. High-quality, labeled data is the lifeblood of effective AI model training, but real-world data is often scarce, imbalanced, or inaccessible due to privacy concerns. Synthetic data as a service enables enterprises to generate large volumes of realistic, balanced, and customizable datasets tailored to specific use cases, drastically reducing the time and cost associated with traditional data collection and annotation. This is particularly crucial for industries such as autonomous vehicles, financial services, and healthcare, where obtaining real data is either prohibitively expensive or fraught with ethical and legal complexities. The ability to augment or entirely replace real datasets with synthetic alternatives is transforming the pace and scale of AI innovation globally.

Furthermore, the market is witnessing robust investments in advanced synthetic data generation technologies, including generative adversarial networks (GANs), variational autoencoders, and diffusion models. These technologies are enabling the creation of highly realistic synthetic data across modalities such as tabular, image, text, and video. As a result, the adoption of synthetic data as a service is expanding beyond traditional use cases like data privacy and AI training to include fraud detection, system testing, and data augmentation for rare events. The growing ecosystem of synthetic data vendors, coupled with increasing awareness among enterprises of its strategic value, is creating a fertile environment for sustained market expansion.

Regionally, North America continues to lead the synthetic data as a service market, accounting for the largest share in 2024, driven by early adoption of AI technologies, strong regulatory frameworks, and a vibrant ecosystem of technology providers. Europe is following closely, propelled by stringent GDPR compliance requirements and a growing focus on responsible AI. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, increased investments in AI infrastructure, and expanding regulatory initiatives around data protection. These regional dynamics are shaping the competitive landscape and driving the global adoption of synthetic data as a service across both established and emerging markets.

The introduction of a Synthetic Data Generation Appliance is revolutionizing how enterprises approach data privacy and security. These appliances are designed to generate synthetic datasets on-premises, providing organizations with greater control over their data generation processes. By leveraging advanced algorithms and machine learning models, these appli

AI in Generative Adversarial Networks (GANs) Market Outlook

According to our latest research, the global AI in Generative Adversarial Networks (GANs) market size reached USD 2.65 billion in 2024, reflecting robust growth driven by rapid advancements in deep learning and artificial intelligence. The market is expected to register a remarkable CAGR of 31.4% from 2025 to 2033, accelerating the adoption of GANs across diverse industries. By 2033, the market is forecasted to achieve a value of USD 32.78 billion, underscoring the transformative impact of GANs in areas such as image and video generation, data augmentation, and synthetic content creation. This trajectory is supported by the increasing demand for highly realistic synthetic data and the expansion of AI-driven applications across enterprise and consumer domains.

A primary growth factor for the AI in Generative Adversarial Networks market is the exponential increase in the availability and complexity of data that organizations must process. GANs, with their unique adversarial training methodology, have proven exceptionally effective for generating realistic synthetic data, which is crucial for industries like healthcare, automotive, and finance where data privacy and scarcity are significant concerns. The ability of GANs to create high-fidelity images, videos, and even text has enabled organizations to enhance their AI models, improve data diversity, and reduce bias, thereby accelerating the adoption of AI-driven solutions. Furthermore, the integration of GANs with cloud-based platforms and the proliferation of open-source GAN frameworks have democratized access to this technology, enabling both large enterprises and SMEs to harness its potential for innovative applications.

Another significant driver for the AI in Generative Adversarial Networks market is the surge in demand for advanced content creation tools in media, entertainment, and marketing. GANs have revolutionized the way digital content is produced by enabling hyper-realistic image and video synthesis, deepfake generation, and automated design. This has not only streamlined creative workflows but also opened new avenues for personalized content, virtual influencers, and immersive experiences in gaming and advertising. The rapid evolution of GAN architectures, such as StyleGAN and CycleGAN, has further enhanced the quality and scalability of generative models, making them indispensable for enterprises seeking to differentiate their digital offerings and engage customers more effectively in a highly competitive landscape.

The ongoing advancements in hardware acceleration and AI infrastructure have also played a pivotal role in propelling the AI in Generative Adversarial Networks market forward. The availability of powerful GPUs, TPUs, and AI-specific chips has significantly reduced the training time and computational costs associated with GANs, making them more accessible for real-time and large-scale applications. Additionally, the growing ecosystem of AI services and consulting has enabled organizations to overcome technical barriers, optimize GAN deployments, and ensure compliance with evolving regulatory standards. As investment in AI research continues to surge, the GANs market is poised for sustained innovation and broader adoption across sectors such as healthcare diagnostics, autonomous vehicles, financial modeling, and beyond.

From a regional perspective, North America continues to dominate the AI in Generative Adversarial Networks market, accounting for the largest share in 2024, driven by its robust R&D ecosystem, strong presence of leading technology companies, and early adoption of AI technologies. Europe follows closely, with significant investments in AI research and regulatory initiatives promoting ethical AI development. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, expanding AI talent pool, and increasing government support for AI innovation. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as enterprises in these regions begin to explore the potential of GANs for industry-specific applications.

Component Analysis

The AI in Generative Adversarial Networks market is segmented by component into software, hardware, and services, each playing a vital role in the ecosystem’s development and adoption. Software solutions constitute the largest share of the market in 2024, reflecting the growing demand for ad

Synthetic Data Generation for Analytics Market Outlook

According to our latest research, the synthetic data generation for analytics market size reached USD 1.7 billion in 2024, with a robust year-on-year expansion reflecting the surging adoption of advanced analytics and AI-driven solutions. The market is projected to grow at a CAGR of 32.8% from 2025 to 2033, culminating in a forecasted market size of approximately USD 22.5 billion by 2033. This remarkable growth is primarily fueled by escalating data privacy concerns, the exponential rise of machine learning applications, and the growing need for high-quality, diverse datasets to power analytics in sectors such as BFSI, healthcare, and IT. As per our latest research, these factors are reshaping how organizations approach data-driven innovation, making synthetic data generation a cornerstone of modern analytics strategies.

A critical growth driver for the synthetic data generation for analytics market is the intensifying focus on data privacy and regulatory compliance. With the enforcement of stringent data protection laws such as GDPR in Europe, CCPA in California, and similar frameworks globally, organizations face mounting challenges in accessing and utilizing real-world data for analytics without risking privacy breaches or non-compliance. Synthetic data generation addresses this issue by creating artificial datasets that closely mimic the statistical properties of real data while stripping away personally identifiable information. This enables enterprises to continue innovating in analytics, machine learning, and AI development without compromising user privacy or running afoul of regulatory mandates. The increasing adoption of privacy-by-design principles across industries further propels the demand for synthetic data solutions, as organizations seek to future-proof their analytics pipelines against evolving legal landscapes.

Another significant factor accelerating market growth is the explosive demand for training data in machine learning and AI applications. As enterprises across sectors such as healthcare, finance, automotive, and retail harness AI to drive automation, personalization, and predictive analytics, the need for large, high-quality, and diverse datasets has never been greater. However, sourcing, labeling, and managing real-world data is often expensive, time-consuming, and fraught with ethical and logistical challenges. Synthetic data generation platforms offer a scalable and cost-effective alternative, enabling organizations to create virtually unlimited datasets tailored to specific use cases, edge scenarios, or rare events. This capability not only accelerates model development cycles but also enhances model robustness and generalizability, giving companies a decisive edge in the competitive analytics landscape.

Furthermore, the market is witnessing rapid technological advancements, including the integration of generative adversarial networks (GANs), advanced simulation techniques, and domain-specific synthetic data engines. These innovations have significantly improved the fidelity, realism, and utility of synthetic datasets across various data types, including tabular, image, text, video, and time series data. The rise of cloud-native synthetic data platforms and the proliferation of APIs and developer tools have democratized access to these technologies, making it easier for organizations of all sizes to experiment with and deploy synthetic data solutions. As a result, the synthetic data generation for analytics market is marked by increasing vendor activity, strategic partnerships, and venture capital investment, further fueling its expansion across regions and industry verticals.

Regionally, North America remains the largest and most mature market, driven by early technology adoption, robust R&D investments, and the presence of leading AI and analytics companies. However, Asia Pacific is emerging as the fastest-growing region, with countries like China, India, and Japan ramping up investments in digital transformation, smart manufacturing, and healthcare analytics. Europe follows closely, buoyed by strong regulatory frameworks and a vibrant ecosystem of AI startups. The Middle East & Africa and Latin America are also witnessing increased adoption, albeit at a more nascent stage, as governments and enterprises recognize the value of synthetic data in overcoming data scarcity and privacy chal

Synthetic Data Generation for Training LE AI Market Outlook

According to our latest research, the global market size for Synthetic Data Generation for Training LE AI was valued at USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 33.8% projected through the forecast period. By 2033, the market is expected to reach an impressive USD 18.4 billion, reflecting the surging demand for scalable, privacy-compliant, and cost-effective data solutions. The primary growth factor underpinning this expansion is the increasing need for high-quality, diverse datasets to train large enterprise artificial intelligence (LE AI) models, especially as real-world data becomes more restricted due to privacy regulations and ethical considerations.

One of the most significant growth drivers for the Synthetic Data Generation for Training LE AI market is the escalating adoption of artificial intelligence across multiple sectors such as healthcare, finance, automotive, and retail. As organizations strive to build and deploy advanced AI models, the requirement for large, diverse, and unbiased datasets has intensified. However, acquiring and labeling real-world data is often expensive, time-consuming, and fraught with privacy risks. Synthetic data generation addresses these challenges by enabling the creation of realistic, customizable datasets without exposing sensitive information, thereby accelerating AI development cycles and improving model performance. This capability is particularly crucial for industries dealing with stringent data regulations, such as healthcare and finance, where synthetic data can be used to simulate rare events, balance class distributions, and ensure regulatory compliance.

Another pivotal factor propelling the growth of the Synthetic Data Generation for Training LE AI market is the technological advancements in generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other deep learning techniques. These innovations have significantly enhanced the fidelity, scalability, and versatility of synthetic data, making it nearly indistinguishable from real-world data in many applications. As a result, organizations can now generate high-resolution images, complex tabular datasets, and even nuanced audio and video samples tailored to specific use cases. Furthermore, the integration of synthetic data solutions with cloud-based platforms and AI development tools has democratized access to these technologies, allowing both large enterprises and small-to-medium businesses to leverage synthetic data for training, testing, and validation of LE AI models.

The increasing focus on data privacy and security is also fueling market growth. With regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are under immense pressure to safeguard personal and sensitive information. Synthetic data offers a compelling solution by allowing businesses to generate artificial datasets that retain the statistical properties of real data without exposing any actual personal information. This not only mitigates the risk of data breaches and compliance violations but also enables seamless data sharing and collaboration across departments and organizations. As privacy concerns continue to mount, the adoption of synthetic data generation technologies is expected to accelerate, further driving the growth of the market.

From a regional perspective, North America currently dominates the Synthetic Data Generation for Training LE AI market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of leading technology companies, robust R&D investments, and a mature AI ecosystem have positioned North America as a key innovation hub for synthetic data solutions. Meanwhile, Asia Pacific is anticipated to witness the highest CAGR during the forecast period, driven by rapid digital transformation, government initiatives supporting AI adoption, and a burgeoning startup landscape. Europe, with its strong emphasis on data privacy and security, is also emerging as a significant market, particularly in sectors such as healthcare, automotive, and finance.

Component Analysis

The Component segment of the Synthetic Data Generation for Training LE AI market is primarily divided into Software and

including both sunny and cloudy days.

Dataset Title

Skin Disease GAN-Generated and Original Images Lightweight Dataset

General Description

This dataset is a collection of skin disease images generated using a Generative Adversarial Network (GAN) approach. Specifically, a GAN was utilized with Stable Diffusion as the generator and a transformer-based discriminator to create realistic images of various skin diseases. The GAN approach enhances the accuracy and realism of the generated images, making this dataset a valuable resource for machine learning and computer vision applications in dermatology.

Creation Process

To create this dataset, a series of Low-Rank Adaptations (LoRAs) were generated for each disease category. These LoRAs were trained on the base dataset with 60 epochs and 30,000 steps using OneTrainer. Images were then generated for the following disease categories:

• Herpes
• Measles
• Chickenpox
• Monkeypox

Due to the availability of ample public images, Melanoma was excluded from the generation process. The Fooocus API served as the generator within the GAN framework, creating images based on the LoRAs.

To ensure quality and accuracy, a transformer-based discriminator was employed to verify the generated images, classifying them into the correct disease categories.

Sources

The original base dataset used to create this GAN-based dataset includes reputable sources such as:

2019 HAM10000 Challenge - Kaggle - Google Images - Dermnet NZ - Bing Images - Yandex - Hellenic Atlas - Dermatological Atlas The LoRAs and their recommended weights for generating images are available for download on our CivitAi profile. You can refer to this profile for detailed instructions and access to the LoRAs used in this dataset.

Dataset Contents

Generated Images: High-quality images of skin diseases generated via GAN with Stable Diffusion, using transformer-based discrimination for accurate classification.

Categories

• Herpes
• Measles
• Chickenpox
• Monkeypox Each image corresponds to one of these four categories, providing a reliable set of generated data for training and evaluation. Melanoma was excluded from generation due to the abundance of public data.

Suggested Use Cases

This dataset is suitable for:

• Image Classification and Augmentation Tasks: Training and evaluating models in skin disease classification, with additional augmentation from generated images.
• Research in Dermatology and GAN Techniques: Investigating the effectiveness of GANs for generating medical images, as well as exploring the use of transformer-based discrimination.
• Educational Projects in AI and Medicine: Offering insights into image generation for diagnostic purposes, combining GANs and Stable Diffusion with transformers for medical datasets.

Citation

Garcia-Espinosa, E. ., Ruiz-Castilla, J. S., & Garcia-Lamont, F. (2025). Generative AI and Transformers in Advanced Skin Lesion Classification applied on a mobile device. International Journal of Combinatorial Optimization Problems and Informatics, 16(2), 158–175. https://doi.org/10.61467/2007.1558.2025.v16i2.1078

** **

Espinosa, E.G., Castilla, J.S.R., Lamont, F.G. (2025). Skin Disease Pre-diagnosis with Novel Visual Transformers. In: Figueroa-García, J.C., Hernández, G., Suero Pérez, D.F., Gaona García, E.E. (eds) Applied Computer Sciences in Engineering. WEA 2024. Communications in Computer and Information Science, vol 2222. Springer, Cham. https://doi.org/10.1007/978-3-031-74595-9_10

Synthetic Data Generation for AI Market Outlook

According to our latest research, the global synthetic data generation for AI market size reached USD 1.42 billion in 2024, demonstrating robust momentum driven by the accelerating adoption of artificial intelligence across multiple industries. The market is projected to expand at a CAGR of 35.6% from 2025 to 2033, with the market size expected to reach USD 20.19 billion by 2033. This extraordinary growth is primarily attributed to the rising demand for high-quality, diverse datasets for training AI models, as well as increasing concerns around data privacy and regulatory compliance.

One of the key growth factors propelling the synthetic data generation for AI market is the surging need for vast, unbiased, and representative datasets to train advanced machine learning models. Traditional data collection methods are often hampered by privacy concerns, data scarcity, and the risk of bias, making synthetic data an attractive alternative. By leveraging generative models such as GANs and VAEs, organizations can create realistic, customizable datasets that enhance model accuracy and performance. This not only accelerates AI development cycles but also enables businesses to experiment with rare or edge-case scenarios that would be difficult or costly to capture in real-world data. The ability to generate synthetic data on demand is particularly valuable in highly regulated sectors such as finance and healthcare, where access to sensitive information is restricted.

Another significant driver is the rapid evolution of AI technologies and the growing complexity of AI-powered applications. As organizations increasingly deploy AI in mission-critical operations, the need for robust testing, validation, and continuous model improvement becomes paramount. Synthetic data provides a scalable solution for augmenting training datasets, testing AI systems under diverse conditions, and ensuring resilience against adversarial attacks. Moreover, as regulatory frameworks like GDPR and CCPA impose stricter controls on personal data usage, synthetic data offers a viable path to compliance by enabling the development and validation of AI models without exposing real user information. This dual benefit of innovation and compliance is fueling widespread adoption across industries.

The market is also witnessing considerable traction due to the rise of edge computing and the proliferation of IoT devices, which generate enormous volumes of heterogeneous data. Synthetic data generation tools are increasingly being integrated into enterprise AI workflows to simulate device behavior, user interactions, and environmental variables. This capability is crucial for industries such as automotive (for autonomous vehicles), healthcare (for medical imaging), and retail (for customer analytics), where the diversity and scale of data required far exceed what can be realistically collected. As a result, synthetic data is becoming an indispensable enabler of next-generation AI solutions, driving innovation and operational efficiency.

From a regional perspective, North America continues to dominate the synthetic data generation for AI market, accounting for the largest revenue share in 2024. This leadership is underpinned by the presence of major AI technology vendors, substantial R&D investments, and a favorable regulatory environment. Europe is also emerging as a significant market, driven by stringent data protection laws and strong government support for AI innovation. Meanwhile, the Asia Pacific region is expected to witness the fastest growth rate, propelled by rapid digital transformation, burgeoning AI startups, and increasing adoption of cloud-based solutions. Latin America and the Middle East & Africa are gradually catching up, supported by government initiatives and the expansion of digital infrastructure. The interplay of these regional dynamics is shaping the global synthetic data generation landscape, with each market presenting unique opportunities and challenges.

Component Analysis

The synthetic data gen

The Artificial Intelligence Synthetic Data Service market is poised for substantial expansion, projected to reach a significant valuation by 2033. This growth is fueled by the escalating demand for high-quality, diverse, and privacy-preserving datasets across various industries. Organizations are increasingly recognizing synthetic data as a critical enabler for accelerating AI model development, testing, and deployment, especially in scenarios where real-world data is scarce, sensitive, or biased. The market's robust CAGR (estimated at a healthy 25-30% given the current AI landscape) signifies a strong upward trajectory, driven by advancements in generative AI techniques and the need to overcome limitations associated with traditional data acquisition methods. Key sectors like autonomous vehicles, healthcare, finance, and retail are at the forefront of adopting synthetic data to train complex algorithms and ensure compliance with stringent data privacy regulations. The market's dynamism is further shaped by evolving trends such as the rise of cloud-based synthetic data generation platforms, offering scalability and accessibility, and the increasing sophistication of on-premises solutions for enterprises requiring maximum control and security. While the widespread adoption of synthetic data presents immense opportunities, certain restraints, like the perception of synthetic data quality and the need for specialized expertise to generate realistic and unbiased datasets, need to be addressed. However, continuous innovation in generative adversarial networks (GANs) and other AI models is steadily mitigating these concerns. The competitive landscape, featuring prominent players like Synthesis, Datagen, and Rendered, is characterized by strategic partnerships, technological advancements, and a focus on catering to niche applications, further propelling the market's overall growth and maturity.

Synthetic AMI Data Generation Market Outlook

According to our latest research, the global Synthetic AMI Data Generation Market size in 2024 stands at USD 412 million, with a robust CAGR of 17.8% anticipated through the forecast period. By 2033, the market is projected to reach USD 1,700 million, driven by the increasing adoption of advanced metering infrastructure (AMI) and the growing demand for high-quality synthetic data to power analytics, AI, and machine learning applications across the energy sector. This growth is propelled by utilities and smart grid solution providers seeking secure, scalable, and privacy-compliant solutions for data-driven innovation.

A primary growth factor for the Synthetic AMI Data Generation Market is the surging need for data privacy and regulatory compliance in the energy and utilities sector. As utilities integrate more digital and IoT-based solutions, the volume of sensitive customer and operational data has increased exponentially. Generating synthetic AMI data enables organizations to develop, test, and validate analytics models without exposing real customer information, thus adhering to stringent data protection regulations such as GDPR and CCPA. This approach not only mitigates risks associated with data breaches but also accelerates the deployment of AI-driven solutions for grid optimization, predictive maintenance, and customer engagement. The emphasis on privacy-preserving data generation is expected to intensify as utilities increasingly leverage data for strategic decision-making and innovation.

Another significant driver for market expansion is the rapid digital transformation of the energy sector, marked by the proliferation of smart meters and the evolution of smart grids. The deployment of AMI systems generates massive datasets that are invaluable for grid analytics, load forecasting, demand response, and meter data management. However, real-world data is often fragmented, incomplete, or difficult to access due to privacy concerns. Synthetic data generation bridges this gap by providing high-fidelity, statistically similar datasets that can be used for algorithm training, scenario simulation, and research and development. This capability is especially crucial for utilities and solution providers aiming to accelerate innovation cycles, improve operational efficiency, and enhance service reliability in a competitive landscape.

The market is also benefiting from advancements in artificial intelligence and machine learning technologies, which have enhanced the accuracy and realism of synthetic data generation tools. Modern synthetic data platforms leverage generative adversarial networks (GANs) and other deep learning techniques to produce highly realistic interval, load profile, and event data. This technological progress not only improves the utility of synthetic datasets for advanced analytics but also reduces the costs and time associated with traditional data collection and annotation. Furthermore, the integration of synthetic data solutions with cloud platforms and meter data management systems is streamlining workflows for utilities, energy retailers, and research institutions, thereby expanding the addressable market and fostering greater adoption across regions.

Regionally, North America leads the Synthetic AMI Data Generation Market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is at the forefront due to its advanced smart grid infrastructure, strong regulatory frameworks, and high levels of investment in digital transformation initiatives. Europe is witnessing significant growth, driven by the EU’s emphasis on energy efficiency, grid modernization, and data privacy. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by rapid urbanization, expanding smart meter deployments, and increasing investments in smart grid technologies in countries such as China, Japan, and India. Latin America and the Middle East & Africa are also showing promising potential, albeit from a smaller base, as governments and utilities begin to prioritize digital infrastructure and data-driven energy management.

Component Analysis

The Component segment of the Synthetic AMI Data Generation Market is bifurcated into software and services, each playing a pivotal role in supporting the evolving needs of utilities, energy retailers, and smart grid solution

Diabetic retinopathy (DR) is a prominent reason of blindness globally, which is a diagnostically challenging disease owing to the intricate process of its development and the human eye’s complexity, which consists of nearly forty connected components like the retina, iris, optic nerve, and so on. This study proposes a novel approach to the identification of DR employing methods such as synthetic data generation, K- Means Clustering-Based Binary Grey Wolf Optimizer (KCBGWO), and Fully Convolutional Encoder-Decoder Networks (FCEDN). This is achieved using Generative Adversarial Networks (GANs) to generate high-quality synthetic data and transfer learning for accurate feature extraction and classification, integrating these with Extreme Learning Machines (ELM). The substantial evaluation plan we have provided on the IDRiD dataset gives exceptional outcomes, where our proposed model gives 99.87% accuracy and 99.33% sensitivity, while its specificity is 99. 78%. This is why the outcomes of the presented study can be viewed as promising in terms of the further development of the proposed approach for DR diagnosis, as well as in creating a new reference point within the framework of medical image analysis and providing more effective and timely treatments.

AI in Synthetic Data Market Outlook

According to our latest research, the AI in Synthetic Data market size reached USD 1.32 billion in 2024, reflecting an exceptional surge in demand across various industries. The market is poised to expand at a CAGR of 36.7% from 2025 to 2033, with the forecasted market size expected to reach USD 21.38 billion by 2033. This remarkable growth trajectory is driven by the increasing necessity for privacy-preserving data solutions, the proliferation of AI and machine learning applications, and the rapid digital transformation across sectors. As per our latest research, the market’s robust expansion is underpinned by the urgent need to generate high-quality, diverse, and scalable datasets without compromising sensitive information, positioning synthetic data as a cornerstone for next-generation AI development.

One of the primary growth factors for the AI in Synthetic Data market is the escalating demand for data privacy and compliance with stringent regulations such as GDPR, HIPAA, and CCPA. Enterprises are increasingly leveraging synthetic data to circumvent the challenges associated with using real-world data, particularly in industries like healthcare, finance, and government, where data sensitivity is paramount. The ability of synthetic data to mimic real-world datasets while ensuring anonymity enables organizations to innovate rapidly without breaching privacy laws. Furthermore, the adoption of synthetic data significantly reduces the risk of data breaches, which is a critical concern in today’s data-driven economy. As a result, organizations are not only accelerating their AI and machine learning initiatives but are also achieving compliance and operational efficiency.

Another significant driver is the exponential growth in AI and machine learning adoption across diverse sectors. These technologies require vast volumes of high-quality data for training, validation, and testing purposes. However, acquiring and labeling real-world data is often expensive, time-consuming, and fraught with privacy concerns. Synthetic data addresses these challenges by enabling the generation of large, labeled datasets that are tailored to specific use cases, such as image recognition, natural language processing, and fraud detection. This capability is particularly transformative for sectors like automotive, where synthetic data is used to train autonomous vehicle algorithms, and healthcare, where it supports the development of diagnostic and predictive models without exposing patient information.

Technological advancements in generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have further propelled the market. These innovations have significantly improved the realism, diversity, and utility of synthetic data, making it nearly indistinguishable from real-world data in many applications. The synergy between synthetic data generation and advanced AI models is enabling new possibilities in areas like computer vision, speech synthesis, and anomaly detection. As organizations continue to invest in AI-driven solutions, the demand for synthetic data is expected to surge, fueling further market expansion and innovation.

From a regional perspective, North America currently leads the AI in Synthetic Data market due to its early adoption of AI technologies, strong presence of leading technology companies, and supportive regulatory frameworks. Europe follows closely, driven by its rigorous data privacy regulations and a burgeoning ecosystem of AI startups. The Asia Pacific region is emerging as a lucrative market, propelled by rapid digitalization, government initiatives, and increasing investments in AI research and development. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as organizations in these regions begin to recognize the value of synthetic data for digital transformation and innovation.

Component Analysis

The AI in Synthetic Data market is segmented by component into Software and Services, each playing a pivotal role in the industry’s growth. Software solutions dominate the market, accounting for the largest share in 2024, as organizations increasingly adopt advanced platforms for data generation, management, and integration. These software platforms leverage state-of-the-art generative AI models that enable users to create highly realistic and customizab

Synthetic Data for Computer Vision Market Outlook

According to our latest research, the global synthetic data for computer vision market size reached USD 420 million in 2024, with a robust year-over-year growth underpinned by the surging demand for advanced AI-driven visual systems. The market is expected to expand at a compelling CAGR of 34.2% from 2025 to 2033, culminating in a forecasted market size of approximately USD 4.9 billion by 2033. This accelerated growth is primarily driven by the increasing adoption of synthetic data to overcome data scarcity, privacy concerns, and the need for scalable, diverse datasets to train computer vision models efficiently and ethically.

The primary growth factor fueling the synthetic data for computer vision market is the exponential rise in AI and machine learning applications across various industries. As organizations strive to enhance their computer vision systems, the demand for large, annotated, and diverse datasets has become paramount. However, acquiring real-world data is often expensive, time-consuming, and fraught with privacy and regulatory challenges. Synthetic data, generated through advanced simulation and rendering techniques, addresses these issues by providing high-quality, customizable datasets that can be tailored to specific use cases. This not only accelerates the training of AI models but also significantly reduces costs and mitigates the risks associated with sensitive data, making it an indispensable tool for enterprises seeking to innovate rapidly.

Another significant driver is the rapid advancement of simulation technologies and generative AI models, such as GANs (Generative Adversarial Networks), which have dramatically improved the realism and utility of synthetic data. These technologies enable the creation of highly realistic images, videos, and 3D point clouds that closely mimic real-world scenarios. As a result, industries such as automotive (for autonomous vehicles), healthcare (for medical imaging), and security & surveillance are leveraging synthetic data to enhance the robustness and accuracy of their computer vision systems. The ability to generate rare or dangerous scenarios that are difficult or unethical to capture in real life further amplifies the value proposition of synthetic data, driving its adoption across safety-critical domains.

Furthermore, the growing emphasis on data privacy and regulatory compliance, especially in regions with stringent data protection laws like Europe and North America, is propelling the adoption of synthetic data solutions. By generating artificial datasets that do not contain personally identifiable information, organizations can sidestep many of the legal and ethical hurdles associated with using real-world data. This is particularly relevant in sectors such as healthcare and retail, where data sensitivity is paramount. As synthetic data continues to gain regulatory acceptance and technological maturity, its role in supporting compliant, scalable, and bias-mitigated AI development is expected to expand significantly, further boosting market growth.

Synthetic Training Data is becoming increasingly vital in the realm of AI development, particularly for computer vision applications. By leveraging synthetic training data, developers can create expansive and diverse datasets that are not only cost-effective but also free from the biases often present in real-world data. This approach allows for the simulation of numerous scenarios and conditions, providing a robust foundation for training AI models. As a result, synthetic training data is instrumental in enhancing the accuracy and reliability of computer vision systems, making it an indispensable tool for industries aiming to innovate and improve their AI-driven solutions.

Regionally, North America currently leads the synthetic data for computer vision market, driven by the presence of major technology companies, robust R&D investments, and early adoption across key industries. However, Asia Pacific is emerging as a high-growth region, fueled by rapid industrialization, expanding AI research ecosystems, and increasing government support for digital transformation initiatives. Europe also exhibits strong momentum, underpinned by a focus on privacy-preserving AI solutions and regulatory compliance. Collectively, these regional trends underscore a global sh

DeepFake electrocardiograms: the beginning of the end for privacy issues in medicine

Paper | GitHub | Original-data-source | PyPI

Content

Big data is needed to implement personalized medicine, but privacy issues are a prevalent problem for collecting data and sharing them between researchers. A solution is synthetic data generated to represent real dataset carrying similar information. Here, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs). We have developed and compare two methods, namely WaveGAN* and Pulse2Pulse GAN. We trained the GANs with 7,233 real normal ECG to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. These synthetic ECGs are fully anonymous and cannot be referred to any individual, hence they may be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using adversarial neural networks on normal ECGs from two population studies, i.e., there by solving the relevant privacy issues in medical datasets.

Citation (cite this paper to use this dataset in your research)

@article{thambawita2021deepfake,
 title={DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine},
 author={Thambawita, Vajira and Isaksen, Jonas L and Hicks, Steven A and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Ellervik, Christina and Olesen, Morten Salling and Hansen, Torben and others},
 journal={Scientific reports},
 volume={11},
 number={1},
 pages={1--8},
 year={2021},
 publisher={Nature Publishing Group}
}

Synthetic Data Platform Market Outlook

As per our latest research, the global synthetic data platform market size reached USD 1.42 billion in 2024, demonstrating robust growth driven by the increasing demand for privacy-preserving data solutions and AI model training. The market is expected to expand at a remarkable CAGR of 34.8% from 2025 to 2033, reaching a forecasted market size of USD 19.12 billion by 2033. This rapid expansion is primarily attributed to the growing need for high-quality, scalable, and diverse datasets that comply with stringent data privacy regulations and support advanced analytics and machine learning initiatives across various industries.

One of the primary growth factors propelling the synthetic data platform market is the escalating adoption of artificial intelligence (AI) and machine learning (ML) technologies across sectors such as BFSI, healthcare, automotive, and retail. As organizations increasingly rely on AI-driven insights for decision-making, the demand for large, diverse, and high-quality datasets has surged. However, access to real-world data is often restricted due to privacy concerns, regulatory constraints, and the risk of data breaches. Synthetic data platforms address these challenges by generating artificial datasets that closely mimic real-world data while ensuring data privacy and compliance. This capability not only accelerates AI development but also reduces the risk of exposing sensitive information, thereby fueling the market’s growth.

Another significant driver is the rising importance of data privacy and protection, particularly in the wake of global regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. Organizations are under increasing pressure to protect consumer data and avoid regulatory penalties. Synthetic data platforms enable businesses to create anonymized datasets that retain the statistical properties and utility of original data, making them invaluable for testing, analytics, and model training without compromising privacy. This ability to balance innovation with compliance is a key factor boosting the adoption of synthetic data solutions.

Furthermore, the synthetic data platform market is benefiting from the growing complexity and volume of data generated by digital transformation initiatives, IoT devices, and connected systems. Traditional data collection methods are often time-consuming, expensive, and limited by accessibility issues. Synthetic data platforms offer a scalable and cost-effective alternative, allowing organizations to generate customized datasets for various use cases, including fraud detection, data augmentation, and software testing. This flexibility is particularly valuable in industries where real data is scarce, sensitive, or costly to obtain, thereby driving further market expansion.

Regionally, North America currently dominates the synthetic data platform market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology companies, robust investments in AI research, and stringent regulatory frameworks in these regions are key contributors to market growth. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, increasing adoption of AI technologies, and supportive government policies. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a relatively slower pace, as organizations in these regions begin to recognize the value of synthetic data in driving innovation and ensuring compliance.

Component Analysis

The synthetic data platform market by component is broadly segmented into software and services. The software segment currently holds the largest market share, as organizations across industries are increasingly investing in advanced synthetic data generation tools to address their growing data needs. These software solutions leverage cutting-edge technologies such as generative adversarial networks (GANs), variational autoencoders, and other machine learning algorithms to create highly realistic synthetic datasets. The ability of these platforms to generate data that closely resembles real-world scenarios, while ensuring privacy and compliance, is a major factor contributing to their widespread adoption.

Within the software segment, vendors are focusing on enhancing the scalability, flexibil

IntroductionAge-related macular degeneration (AMD) is one of the leading causes of vision impairment globally and early detection is crucial to prevent vision loss. However, the screening of AMD is resource dependent and demands experienced healthcare providers. Recently, deep learning (DL) systems have shown the potential for effective detection of various eye diseases from retinal fundus images, but the development of such robust systems requires a large amount of datasets, which could be limited by prevalence of the disease and privacy of patient. As in the case of AMD, the advanced phenotype is often scarce for conducting DL analysis, which may be tackled via generating synthetic images using Generative Adversarial Networks (GANs). This study aims to develop GAN-synthesized fundus photos with AMD lesions, and to assess the realness of these images with an objective scale.MethodsTo build our GAN models, a total of 125,012 fundus photos were used from a real-world non-AMD phenotypical dataset. StyleGAN2 and human-in-the-loop (HITL) method were then applied to synthesize fundus images with AMD features. To objectively assess the quality of the synthesized images, we proposed a novel realness scale based on the frequency of the broken vessels observed in the fundus photos. Four residents conducted two rounds of gradings on 300 images to distinguish real from synthetic images, based on their subjective impression and the objective scale respectively.Results and discussionThe introduction of HITL training increased the percentage of synthetic images with AMD lesions, despite the limited number of AMD images in the initial training dataset. Qualitatively, the synthesized images have been proven to be robust in that our residents had limited ability to distinguish real from synthetic ones, as evidenced by an overall accuracy of 0.66 (95% CI: 0.61–0.66) and Cohen’s kappa of 0.320. For the non-referable AMD classes (no or early AMD), the accuracy was only 0.51. With the objective scale, the overall accuracy improved to 0.72. In conclusion, GAN models built with HITL training are capable of producing realistic-looking fundus images that could fool human experts, while our objective realness scale based on broken vessels can help identifying the synthetic fundus photos.

This dataset contains network traffic data collected from a computer network. The network consists of various devices, such as computers, servers, and routers, interconnected to facilitate communication and data exchange. The dataset captures different types of network activities, including normal network traffic as well as various network anomalies and attacks. It provides a comprehensive view of the network behavior and can be used for studying network security, intrusion detection, and anomaly detection algorithms. The dataset includes features such as source and destination IP addresses, port numbers, protocol types, packet sizes, and timestamps, enabling detailed analysis of network traffic patterns and characteristics and so on... The second file in this dataset contains synthetic data that has been generated using a Generative Adversarial Network (GAN). GANs are a type of deep learning model that can learn the underlying patterns and distributions of a given dataset and generate new synthetic samples that resemble the original data. In this case, the GAN has been trained on the network traffic data from the first file to learn the characteristics and structure of the network traffic. The generated synthetic data in the second file aims to mimic the patterns and behavior observed in real network traffic. This synthetic data can be used for various purposes, such as augmenting the original dataset, testing the robustness of machine learning models, or exploring different scenarios in network analysis.