Healthcare innovation involves synthetic data. Reply uses Artificial Intelligence models to generate highly accurate synthetic data, ensuring patient privacy and allowing accurate forecasts.
Synthetic data represents an effective solution when the sensitivity of data becomes a limitation for their management and sharing. This data is artificially generated through the use of generative adversarial network (GAN) models starting from existing real data samples, and makes it possible to enrich and optimise the original datasets, in an efficient way both in terms of time and resources.
In the healthcare sector, accessing a vast pool of data to formulate accurate forecasts is a complex task. This is not only due to strict regulations that impose limits on sharing and accessing such information, but also because very often this data is fragmented across various medical facilities and storage systems. This makes it difficult to collect a sufficient volume of data. The use of synthetic data in this context can provide an advantageous solution, as they allow the generation of realistic information that can be used for analysis, research and forecasts without compromising patient confidentiality.
To enhance the security of sensitive data, it is possible to adopt techniques that ensure maximum privacy protection, such as “differential privacy”. This approach involves inserting random noise into real data to hide sensitive information, thus allowing the Generative AI model to use this data safely, without compromising the accuracy of the generated synthetic dataset. As a result, differentially trained Generative AI models guarantee the correct protection of privacy during the learning process.
Reply is actively applying the potential of synthetic data in various areas of the healthcare sector, such as the creation of advanced datasets for hospital documents, or the enrichment of datasets for research on Parkinson's disease. Furthermore, in healthcare solutions, it is introducing generative models with the aim of improving interaction with patients. At the same time, it is making a significant contribution to clinical research, introducing the use of synthetic patients to test digital medicine platforms.
These advances promise to open up new perspectives for the healthcare sector, encouraging more personalised and effective care for patients.
Expanding the dataset
Increasing the volume of training data is critical to improving the generalisation capacity and performance of models.
Guaranteed security and privacy
Generating synthetic data that is realistic and compliant with privacy regulations allows you to effectively address data privacy concerns and compliance requirements.
The creation of balanced datasets ensures a uniform distribution between different categories of data, thus helping to improve the effectiveness of training and evaluation of models.
Efficiency in terms of time and resources
Reducing the time spent preparing data allows data scientists to focus on model development, minimising the risks associated with real-world testing.