Three Steps to Check While Using Synthetic Data in Clinical Trials, No Matter How You Are Adopting Machine Learning In Clinical Trials
Synthetic Data in Clinical Trials and the Healthcare Industry
Artificial Intelligence (AI) and Machine Learning (ML) can create synthetic data. Synthetic data has grown extremely popular in various industries because it cuts down on the time required to gather and prepare the data. It also can help preserve anonymity. The healthcare industry has also started taking a serious look at this technology and adopting it for clinical trials to speed them up, find cures for diseases quickly, and ensure compliance with data privacy laws while sharing sensitive personally identifying data.
This method of generating realistic data saves money and resources and allows AI engineers to create realistic data sets rapidly. Whereas in the past, collecting, evaluating, and deconstructing the data could take months, with AI and ML, data engineers can often reduce the process to a matter of hours. It also allows researchers to obtain as much data as they need.
However, although the benefits of this method are many, limitations exist. These limitations come with anything that automates or removes the human element. Users of this data must have an awareness of these limitations and consider them carefully when applying the synthetic data to clinical trials and other uses in the healthcare industry.
Limitations of Artificial Intelligence, Machine Learning, and Synthetic Data
It is easy to dive in and move forward with synthetic data because of the desire to reduce the amount of time that it takes to get a potentially life-saving drug out to market, finish a clinical trial sooner, or get information out to doctors that could potentially help with more quickly identifying conditions and determining the best course of treatment.
Here we take a quick look at the main limitations of Artificial Intelligence, Machine Learning, and Synthetic Data.
