Objectives: The aim of this perspective is to report the use of synthetic data as a viable method in women's health given the current challenges linked to obtaining life-course data within a short period of time and accessing electronic healthcare data.
Methods: We used a 3-point perspective method to report an overview of data science, common applications, and ethical implications.
Results: There are several ethical challenges linked to using real-world data, consequently, generating synthetic data provides an alternative method to conduct comprehensive research when used effectively. The use of clinical characteristics to develop synthetic data is a useful method to consider. Aligning this data as closely as possible to the clinical phenotype would enable researchers to provide data that is very similar to that of the real-world.
Discussion: Population diversity and disease characterisation is important to optimally use data science. There are several artificial intelligence techniques that can be used to develop synthetic data.
Conclusion: Synthetic data demonstrates promise and versatility when used efficiently aligned to clinical problems. Therefore, exploring this option as a viable method in women's health, in particular for epidemiology may be useful.
Keywords: Electronic health records; Machine learning; Real-world Data; Synthetic data.
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