Synthetic Data In Healthcare – Will Smarter Data Bring The A.I. Revolution In Healthcare?

As the importance of A.I. grows in modern medicine, there’s a huge need for data (as well as data annotation) – the latter being one of the most important aspects of the work in building an algorithm. Is synthetic data fake? Moreover, it’s possible to use methods that ensure that synthetic data very much resembles the real one. Without patient data, there is no A.I. in healthcare. Above all that, privacy concerns limit the amount of available data in medicine. That is where synthetic data could be of help. It can fill in the missing data, making it possible to produce entirely fabricated patient datasets that are just as useful for training A.I. as the real thing, while keeping patient data protected. Synthetic data could help overcome this challenge as the training could focus on such variables, making use of real-world environments. Synthetic data already has a number of practical use cases. By using synthetic data trained on much larger datasets, their algorithm was “better able to learn what to look for in our pathology images” – Dr. Todd Hollon, neurosurgeon and principal investigator of the machine learning in neurosurgery laboratory at Michigan Medicine explained.