Synthetic Data Pipelines for Training AI Models in Data-Scarce Domains

The success of AI models largely depends on the availability of large and high-quality datasets. However, in many real-world scenarios—such as medical diagnostics, remote sensing, or low-resource languages— gathering such data is difficult or even impossible due to privacy, cost, or scarcity. Synthetic data offers a compelling alternative. This paper proposes a comprehensive synthetic data pipeline tailored for data-scarce domains. It includes domain understanding, synthetic data generation using simulation, generative models (e.g., GANs, diffusion models), data augmentation, validation, and integration with downstream AI models. Experimental evaluation in healthcare imaging and industrial defect detection demonstrates improved model accuracy and generalizability using synthetic data pipelines.