Over the past 60 years, the life expectancy of newborns has increased by about 20 years – from 52.5 to 72 years as of 2018. During this time, we have witnessed an incredible wave of technological innovations: the rise of the Internet, medical breakthroughs and a deeper understanding of the initiatives of Public health changes the course of human life. And with the emergence of new technologies such as blockchain and artificial intelligence, we know that more radical transformations are coming. These revolutionary technologies pave the way for a longer, healthier life.
To illustrate how healthcare has advanced with these technologies, I will highlight a case study of two unique companies, Insilico Medicine and Longenesis. Together, they show how the development of artificial intelligence in medicine has grown with the advent of blockchain-powered medical applications.
Data Driven Health
In 2014, long-time innovator Alex Zhavoronkov and their company, Insilico Medicine, contacted me. The company was founded on a simple but radical premise: using artificial intelligence to accelerate drug discovery and development. At that time, the use of artificial intelligence was still in progress, both in the public mind and as applied in medicine. But in the seven years since I invested in this company, I’ve used artificial intelligence to completely transform research and development in the treatment sector. Its rapid discovery and development of new treatments is the result of the vast amount of data they process in their search for the second best drug. These data, rich in source and volume, are from genomic and proteomic sequencing of real patients receiving medical treatment. With dozens of new drug candidates, they demonstrate huge potential in using AI for data-driven healthcare services.
However, Insilico’s revolutionary progress has not been without obstacles. Dealing with huge amounts of data presented unique challenges in terms of centralization and security. Health data tends to be scattered and fragmented. Each doctor, medical center, and hospital maintains their own data store, and due to confidentiality rules, data is usually only shared when necessary for patient care. Access to composite patient data was crucial to the success of Insilico’s AI algorithms, and it simply wasn’t available.
Privacy Technology and Blockchain
In search of solutions to the security and centralization issues associated with this type of data, the Alex and Insilico Medicine team soon discovered blockchain and distributed ledger technology. The immutability of blockchain records and the ability to have multiple decentralized nodes contributing data to a shared ledger provide solutions to complex patient data problems. This technology was what they were looking for, but they needed a partner who could develop it with them. Insilico has formed a joint venture with leading European blockchain company Bitfury (now one of the largest technology startups on the continent) and launched a new company called Longenesis. Longenesis’ goal was clear: to create a blockchain ecosystem for healthcare that takes into account the privacy requirements of health data and the application needs of biotechnology research.
Related: Data privacy concerns are growing, and blockchain is the answer
Longenesis has developed a blockchain-based environment for health/biotech stakeholders, including patient organizations, biomedical research groups, research partners, and sponsors. The good thing about Longenesis’ decision is that there is always agreement. When patients agree to provide their data for any purpose, there is indisputable evidence of their consent.
The first product, Curator, is used by hospitals and other healthcare organizations to make available data securely and accurately to researchers without compromising patient privacy. This feature allows researchers to view data sets without compromising the security of patient information. When a researcher or company is interested in using the data, Longenesis’ other product, Engage, provides it. Engage also allows hospitals and researchers to rapidly recruit patients for new clinical trials and research by consistently obtaining patient consent. Whether AI is used to analyze new data from medical research or “old” data from medical records, patients are aware of this and can decide to consent when it suits them.
