This report analyses recent developments in both Artificial Intelligence (AI) and drug R&D. The combination of Machine Learning (ML) and genomics offers to improve our system for treatment development. Yet, the issue of genetic data access and privacy remain. The report focuses on the latest in privacy-first AI. Blockchain is further proposed to create a decentralised and auditable biobank network.
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To find out more about the privacy issues and solutions involved in AI and genomics, download the full report.
The 2020 outbreak of Covid-19 uncovered the failings in our current drug and vaccine discovery. Need forced the timeline down to 12-18 months from 10 years. But, this is an anomaly, and still too long.
On a whole, drug identification and discovery are becoming more difficult and have a higher rate of failure. Working with better data from the outset can improve this. That's where Artificial Intelligence comes in. Using genome sequencing, AI can process large amounts of data very quickly.
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To find out more about the privacy issues and solutions involved in AI and genomics, download the full report.
Drug R&D faces high barriers as medicine becomes more advanced. Discovery and identification processes are becoming more difficult and expensive. One report found that on average, it took 10-12 years and cost US$2 to develop one drug in 2018. The approval rating is only 10%.
More data processing from the outset will improve our drug design system. AI can process large quantities of data extremely quickly. Using genome sequencing, AI can enable vaccine and drug reverse-engineering.
To do this, we first need more genetic information. Improving AI and Machine Learning to sift through this information is the second step.
To find out more about the privacy issues and solutions involved in AI and genomics, download the full report.
Genomes can reveal biomarkers linked to genetic diseases or identify targets for drugs. However, ownership over genetic data is currently unclear and varies based on jurisdiction. Issues around access and storage of genetic information remain. We need concrete solutions to preserve individual privacy and manage consent.
For Europe, the General Data Protection Regulation (GDPR) regulates this. Many member-states have further legislation about health data. Many of these laws provide more research allowances.
As genetic data becomes a more common research tool, we need to address several key objectives:
To find out more about the privacy issues and solutions involved in AI and genomics, download the full report.
While a much-needed debate on privacy is already underway, issues around access and consent for research remain. Estonia's e-Health initiative offers a potential guide. We propose a blockchain access portal based on their X-Road to create a decentralised network. This would connect genetic data storage facilities and researchers around Europe.
A decentralised blockchain network provides several advantages:
Combining blockchain technologies with emerging AI encryption techniques makes greater privacy possible. Blockchain makes this network fully auditable. This improves protection and consent for data contributors. And, technologies to obscure data even as it's used further improves privacy. With this, people could track the studies their genetic data is used in and manage access, all under pseudonymisation.
