Exploring the Untapped Potential of Synthetic Data: A Comprehensive Review
Shashank Agarwal et al. · The Review of Contemporary Scientific and Academic Studies · 2023
Synthetic data generation (SDG) is known as the method of training a model with machine learning techniques to recognize patterns in a real dataset.The trained model can then be used to produce fresh, or synthetic data.Synthetic data generation stands as a pivotal solution at the intersection of data privacy and medical research.This review paper covers in detail synthetic data generation approaches and their role in the field of healthcare.The article first describes the three major types of synthetic data generation approaches.The paper then continues to explore the concept of language modeling that is adapted to AI-generated tools, uses of synthetic data, and its applications in healthcare.Moreover, the paper finally highlights the potential challenges that may be faced by the healthcare industry associated with the adoption of synthetic data.It was concluded that challenges like complexity, bias, and regulation persist, but SDG remains promising for data-driven healthcare.It bridges technology, ethics, and innovation for transformative insights.