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AI, Privacy, and Genomics: The Next Era of Drug Design

Covid-19 remains a pressing problem. AI and genomics can speed vaccine and drug R&D, but we need better privacy first.

Evelyne

Buzziol

August 12, 2020

To learn more about emerging privacy-first AI and what biobank network would bring to Europe's disease response, read our full report,AI, Privacy, and Genomics: The Next Era of Drug Design.

2020 started with the global spread of Covid-19, a novel coronavirus. Countries’ economies, international politics and citizens’ day-to-day lives were all affected. Therefore, authorities and experts turned to drug industries and medical research to find a vaccine urgently.

Who did they turn to?

The first possible matches came from biotech companies using Artificial Intelligence. This is largely because, according to Mark-Jan Harte, CEO of Aidence:

‘AI solutions have the potential to improve healthcare by supporting physicians in making faster, better informed and more accurate decisions’.

AI can offer a workable solution to speed up the drug and vaccine discovery. But, what might be the consequences for our privacy?

Our latest report focuses on the use of AI in healthcare and drug discovery, and the implications on individuals’ right to privacy. Since our genetic data is our identity, it’s imperative to go deeper into privacy. As a result, we need to consider confidentiality, the correct use, and the respect of blood relatives’ privacy.

What are the current potential solutions?

Decoupling genetic data from personal information is not enough to protect individuals’ privacy. Encryption is one of the most promising solutions, as Andy Repton, Security Officer of Aidence, outlined:

‘Homomorphic encryption techniques not only give the ability to provide your encrypted data to a third party and have it processed without ever being readable, bringing a huge asset to privacy but also can theoretically be resistant to quantum computing once/if it reaches maturity, unlike our current standard asymmetric methods of encryption’.

In this report, we explore the new technologies and encryption models in AI, and how each of them can contribute to drug discovery.

Leaders at Gero, Turbine, Quantib, Aidence, Alphanosos, Iktos, and e-Estonia gave us input. Thanks to them, we developed our predictions for the next ten years of medical research:

  • Better collaboration networks will emerge;
  • Genetic privacy will be overhauled;
  • AI will become a fundamental part of drug discovery;
  • The pharmaceutical giants will lose some of their power.

However, the innovations in encryption are not enough. We propose a European decentralised network to connect data providers with researchers. This solves issues of tracking requests, data access, and storage. The EU has already spearheaded the right to privacy with GDPR. Playing off this, AI development could secure our privacy while improving drug R&D.

Blockchain network with hospitals, biobanks & individual cold wallets on one side to biotech, big pharma, wet labs & reserch
Potential blockchain-enable biobank access network.

Overall, the future of medical research needs to be more cost-effective and efficient. Through innovative data-sharing systems, we can improve drug R&D while preserving privacy and data ownership.

Evelyne recently graduated in International Politics and Diplomacy at the University of Padua. She has a background in awareness campaigns and joined the dGen team as Marketing & Content Writer Intern.