However, AI requires its data to be working perfectly in order operate efficiently. If the models are not trained properly on comprehensive, objective, and high-quality data, it could lead to insufficient outcomes. This way, AI has turned out to be lucrative aspect for healthcare institutions. However, it is quite challenging for them to gather and use information while also adhering to privacy and confidentiality regulations because of the sensitivity of the patient data involved.
This is where the idea of ‘synthetic data’ come into play.
Synthetic Data
The U.S. Census Bureau defines synthetic data as artificial microdata that is created with computer algorithms or statistical models to replicate the statistical characteristics of real-world data. It can supplement or replace actual data in public health, health information technology, and healthcare research, sparing companies the headache of obtaining and utilizing real patient data.
One of the reasons why synthetic data is preferred over the real-world information is the privacy it provides.
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