Artificial intelligence and blockchain companies partner to advance healthcare research Posted on : Aug 17 - 2017

Baltimore-based next-generation artificial intelligence company today announced a research collaboration with The Bitfury Group, the world's leading full-service blockchain technology conglomerate, to develop novel solutions for healthcare applications. The companies signed a memorandum of understanding (MOU) to collaborate in the academic and commercial settings to develop AI on Blockchain solutions for the healthcare industry.

"Blockchain can secure and streamline our medical systems, while AI has the potential to revitalize data management and machine learning to help identify trends and diseases," said Valery Vavilov, founder and CEO of The Bitfury Group. "By partnering with Insilico, we will be able to combine their expertise in deep learning and bioinformatics with our Blockchain proficiency and real-time solutions to create bespoke and innovative new products for the healthcare sector."

"The Bitfury Group is one of the most reputable companies in Blockchain developing their own semiconductors and end-to-end Blockchain solutions trusted by the major corporations and governments worldwide. We are happy to enter into a research collaboration with Bitfury to develop innovative solutions that may save lives and extend human healthspan", said Alex Zhavoroknov, PhD, founder and CEO of Insilico Medicine, Inc.

Blockchain and Artificial Intelligence

Recent advances in AI have surpassed human accuracy in image and voice recognition and are transforming multiple industries, including manufacturing, transportation, finance and entertainment. However, the use of AI in the healthcare industry has been comparatively slower than in other sectors, as deep learning systems need a large number of examples to learn from and can require up to tens of millions of data sets to achieve a high level of accuracy.

Next-generation AI developed by Insilico Medicine can be used to validate, assess and improve the quality of biological samples as well as learn using large volumes of heterogeneous data without human intervention. Multiple new methodologies including the feature importance, deep feature selection and deep pathway analysis among the others can provide the biologically-relevant interpretation of the inner workings of the AI systems. View More