Many AI applications in healthcare involve machine learning and deep learning models
Integrating healthcare data among researchers, universities, and firms developing AI solutions has a variety of advantages. However, due to restrictions such as HIPAA, exchanging patient data safely is a significant barrier in the healthcare business. Synthetic data can assist healthcare researchers in creating shareable data and overcoming these limitations.
Improves machine learning model accuracy
Many AI applications in healthcare involve machine learning and deep learning models, like patient data analytics, medical imaging, and medication development. It is critical for successful prediction to feed these algorithms with adequate and reliable training patient data. By extending the training dataset size without breaking data privacy requirements, synthetic data increases machine learning or deep learning model accuracy.
Enables prediction of rare diseases
Clinical trials with a small number of patients provide erroneous results. Synthetic data can be used to construct control groups for clinical studies including uncommon or recently found diseases for which there is insufficient existing data, allowing for the diagnosis of rare diseases.
This is analogous to the advantage of synthetic data in supporting ML model accuracy, although it can be more obvious in circumstances where data is scarce.
Collaboration between medical and pharmaceutical organizations can help doctors identify patients faster and improve medication discovery. Synthetic patient data that mimics the features of real patients can help in collaboration.
Provides reproducibility for medical research
It is critical for scientific development to be able to duplicate the outcomes of a research or experiment. Nevertheless, patient data privacy rules can impede clinical research reproducibility. Clinical researchers can guarantee that their outcomes are reproducible by conducting studies on and sharing synthetic patient databases.
Problems with using synthetic data
When employed in healthcare, synthetic data can have drawbacks.
For starters, it isn’t as valuable as real data. The integrity of clinical synthetic data is heavily influenced by the training data and the data synthesis method. The research team discovered that the experimental group could only match the control team’s results with 70% reliability, which may not be suitable in some instances.
Another issue with synthetic clinical data is the possibility of omitting outliers that would otherwise be included in a real dataset. Data-generation neural networks are terrible in generating unusual-but-possible data sets. Furthermore, outliers are frequently more significant than average data points.
While useful for some applications, the transfer of outliers from an “actual data” training set to a synthetic dataset may raise privacy problems. If a neural network passes outliers in the training sample of patient data into synthetic data, these different data points might possibly be used to identify specific patients.
Furthermore, neural network systems that generate synthetic data are susceptible to cyberattacks and must rely on genuine private data. A hacker who gains access to the data-generating system may be able to reverse engineer confidential information. Although some synthetic data systems use severely restricted access to prevent this type of attack, complete prevention is difficult.