Life-sciences pros believe in potential of AI, blockchain: survey

By Jenni Spinner

- Last updated on GMT

( via Getty Images Plus)
( via Getty Images Plus)

Related tags Blockchain Artificial intelligence Data management Technology

A survey by the Pistoia Alliance indicates most professionals believe the tech can accelerate R&D, but companies are stymied by a skills gap and data bias.

Life sciences group the Pistoia Alliance has released a comprehensive survey gauging life-science professionals’ views on the implementation of artificial intelligence (AI) and blockchain technology in the life sciences. The findings indicate a high level of interest in AI among respondents, with 57% currently engaging in computational drug repurposing.

Also, the findings revealed that awareness of blockchain has increased; 89% now are cognizant of the technology, compared to 82% just three years ago. However, even considering this increase, the survey indicates lack of access to people with relevant blockchain skills remains the biggest barrier to widespread adoption (selected by 30%).

Pistoia Alliance consultant Dr. Becky Upton discussed the survey with Outsourcing-Pharma, and how companies might overcome blockchain barriers.

OSP: Could you please talk about the overall evolution of the use of blockchain in life sciences R&D?

BU: Over the past decade there has been significant hype around blockchain, but we are now seeing this conversation progress as new applications of the technology appear. In life sciences, it shows promise due to its trusted, auditable, transparent and disintermediated system of record.

For example, in clinical trials with its ability to create tamper-proof records it’s likely to become a key part of increasing patient participation as more clinical trials are conducted remotely because of the pandemic. The security advantages should both improve patient trust and facilitate further knowledge sharing across the life science community.

OSP: Despite the increase in understanding of blockchain (up to 89%), there’s still a gap in folks using it (57%). Could you please talk about some of the barriers, and ways to overcome them?

Becky Upton, consultant, The Pistoia Alliance

BU: While the understanding of blockchain is growing, we’re finding that use in the life sciences industry is still highly exploratory and mostly confined to pilots and proof-of-concept studies. The survey identified the biggest barriers preventing it from moving forward are a lack of access to skills (30%), a lack of standards (19%) and interoperability (17%).

Access to the right skills and staff is vital in ensuring blockchain is used effectively. We need to see more collaboration between life science, healthcare and technology companies to counter this – ensuring skills and best practices are shared and developed when needed.

OSP: Pistoia Alliance president Steve Arlington mentions that cross-industry collaboration could help overcome access. Do you have suggestions on how to bring such collaboration about?

BU: Ultimately, it is only by working together to demonstrate the value of new technologies like blockchain, and ensuring companies have access to the right skills and capabilities, that adoptions will become mainstream. At the Pistoia Alliance, we aim to make this collaborative approach a reality; we are doing this through an initiative to establish a proof-of-concept for blockchain technology in the clinical trial informed consent process.

This project grew from a Blockchain Community of Interest which provided an opportunity for life science and healthcare stakeholders – from providers, to researchers and patients – to come together to discuss and better understand the benefits and opportunities as well as the risks of blockchain technology to their domains.

OSP: Please tell us about the Centre of Excellence in AI—how it came about, and how it will work to increase the use and implementation of AI.

BU: We set up the Centre of Excellence in AI and ML to support our members in the understanding and pursuit of innovative new AI and ML applications in life science R&D. Our aim is to find the best use cases, define best practice around the use of the technologies, and turn widespread adoption of AI/ML into a reality; we do this through educational events, webinars, and discussion groups, which cover a range of challenges aimed at kicking off AI projects or prototypes. It also provides a virtual and physical co-working space, enabling life science professionals to share knowledge and learn how best to apply AI, machine, and deep learning to R&D – wherever they are in the world.

OSP: Could you also tell us about the Informed Consent project?

The Pistoia Alliance Informed Consent blockchain project has been set up to demonstrate the value of blockchain technology in underpinning the clinical trial informed consent process. Obtaining and revoking consent in clinical trials is a complex process involving multiple stakeholders, documents and transactions in which the creation and maintenance of a secure audit is imperative for regulatory purposes.

Blockchain enables secure transactions between parties by creating an immutable record of these transactions and storing them as part of a decentralized system.

OSP: Do you have anything else to add about the use of blockchain, AI and other advanced tech?

BU: The pandemic has shown us all how vital digital and emerging technologies are to enable us to keep working, learning and communicating. The shift to digital and online channels is not going to slow down, and this is no different in life sciences.

From labs to learning environments, more scientists are exploring how advanced technologies can further our understanding and help to meet the unmet needs of patients around the world. The key takeaway is that as we’ve seen during the last 12 months, sharing data and knowledge has led to faster breakthroughs, and we must maintain this momentum.

To embed AI and blockchain and realize the benefits, we must seek out opportunities to collaborate on everything from data standards and governance, to new use cases.

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