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- Applications
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- Challenges
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- Trends
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- Conclusion
One of the most important factors that influences decision making across industries is data. The medical industry is no different. From medical research and clinical trials to introducing new treatments, sound medical data is a key component. Generative AI has broken new ground in this field by creating synthetic data that mimics the real-world data, and therefore, has helped accelerate developments in the medical field. This article explores the technology landscape for synthetic data.
Synthetic data adoption is growing and has garnered considerable attention given its diverse applications driving innovation and operational efficiency. Synthetic data generation has emerged as a key solution to challenges like data scarcity, ethical concerns, and privacy issues. Synthetic data generation has transitioned from statistical methods to the advanced realm of Generative AI where these new models excel at learning from complex, unusual, and diverse data characteristics, including multimodal and heavily skewed distributions.
As AI continues to evolve, synthetic data will play an increasingly vital role in business strategy across industries and opening the door to create new revenue opportunities or business models. Few examples are below:
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Data as a Service (DaaS): companies that specialize in generating high-quality synthetic data provide this as a service to businesses that need large, privacy-compliant datasets.
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Subscription models: healthcare companies can offer synthetic datasets on a subscription basis, for example specialized synthetic patient data for rare diseases to pharmaceutical companies.
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Licensing datasets: ecommerce companies can license synthetic datasets to marketing agencies or ad platforms, enabling them to refine customer targeting and personalize ads.