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Unlike other synthetic library providers, NeuTigers’s synthetic data has been clinically validated for a diagnostic solution currently pending FDA approval. The resulting machine learning model has been optimized to be hosted on edge devices such as smartwatches and IoT.
Synthetic data is non-reversible, artificially created data that replicates original data’s statistical characteristics and correlations.
Developing successful artificial intelligence and machine learning models requires access to large amounts of high-quality data. However, collecting healthcare data is often challenging.
Generating synthetic data that reflects the statistical characteristics of real-world data can speed up AI model development at lower a cost.
Synthetic data are inexpensive compared to collecting large datasets, and they do not compromise patients’ privacy. The analyst firm Garter estimates that by 2024, 60% of the data used to develop AI and analytics projects will be synthetically generated.
Synthetic data comes in handy when testing a new product and when not enough data is available yet or when privacy requirements make it challenging to obtain real-world data. Synthetic data can be generated to meet specific needs or conditions not available in existing real data.
Innovative leaders in the life sciences use AI and synthetic data to manage their operations better and accelerate discovery.
Here are a few examples of how the healthcare sector uses AI to optimize performance:
Artificial Intelligence has a tremendous impact on our lives, society, and businesses. Computer vision, autonomous vehicles, speech, language and text processing, facial recognition, sentiment analysis, and search, to name a few, are all examples of functionalities powered by AI.
These applications can be combined and customized to meet the needs of any business process.
As with any new technology seizing the opportunity, developing the vision and creativity to implement AI within a business can be challenging. Yet like with any disruptive innovations, AI implementation must meet a carefully planned business transformation process.
To make your synthetic data project a catalyst for your business model innovation, NeuTigers products and consultants are here to guide you.
Read the academic paper on TUTOR framework from Princeton University
*TUTOR stands for Training Neural Networks Using Decision Rules as Model Priors
