Generative AI like ours implements a large medical model to create synthetic data by learning the patterns and distributions of real-world data. This synthetic data can be used for a variety of purposes, including clinical research, rare disease populations, data analytics, and training models.

In clinical research, synthetic data can be used to supplement real-world data to increase sample sizes, reduce costs, and protect patient privacy. For rare disease populations, synthetic data can be used to generate more data for research, as well as to test hypotheses and develop treatments.

In data analytics, synthetic data can be used to create more diverse datasets and improve the accuracy of predictions. Additionally, synthetic data can be used to train machine learning models, reducing the need for large amounts of real-world data and increasing the efficiency of the training process. Overall, synthetic data has the potential to revolutionize the way we approach research and data analysis in the medical field.