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. 

Synthetic data is created in a way that maintains the dataset's analytical usefulness while replacing any personally identifying information (PII) with non-identified numbers. This ensures that identities cannot be traced back to particular records or used for re-identification while facilitating the easy usage and exchange of data for internal use.

Using fake data as an alternative for PII ensures that the organizations remain true to their guidelines such as GDPR and HIPAA throughout the process. 

In addition to protecting privacy, synthetic datasets can assist save the time and money that businesses often need to spend obtaining and managing real-world data using conventional techniques. Without needing businesses to enter into complicated data-sharing agreements, privacy legislation, or data access restrictions, they faithfully reproduce the original data.

Caution is a Must At All Stages

Even though synthetic data has a lot of advantages over real data, it should never be treated carelessly.

For example, the output may be less dependable and accurate than anticipated and could have an impact on downstream applications if the statistical models and algorithms being used to generate the data are faulty or biased in any manner. In a similar vein, a malicious actor could be able to re-identify the data if it is only partially safeguarded.

Such case can happen if the synthetic data include outliners and unique data points, such as a rare disease found in a small number of records. It may be connected to the original dataset with ease. Re-identifying records in the synthetic data can also be accomplished by adversarial machine learning techniques, particularly in cases where the attacker has access to both the generative model and the synthetic data.

These situations can be avoided by using techniques like differential privacy – to add noise to the data – and disclosure control in the generation process in order to add alteration and perturbation of the information. 

Generating synthetic data could be tricky and may as well result in compromise of transparency and reproducibility. Researchers and teams are thus advised to take the aforementioned approach without running the same risks, and constantly seek to document and share the procedures used to produce synthetic data.