Not everyone is perfect, which is why it is crucial to understand the limitations of synthetic data in clinical trials. It will give you a better idea of what you can do with it and if you can use it in your research.
Here are the top limitations of using synthetic data in clinical trials:
1. Not Always Reliable In Modeling Outcomes
Unfortunately, synthetic data is not always reliable when it comes to modeling outcomes. That is because some researchers found that the predictions of Synthea did not always line up closely enough with the real-world data. The researchers used Synthea AI to generate a population of more than one million Massachusetts residents.
The synthetic residents mirrored the social determinants, demographics, and conditions that one can expect from such a sample of citizens. After that, they tested this data against the real-world incidences of four health quality measures.
These included:
· Controlling high blood pressure
· The rate of complications after a knee or hip replacement
