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.

The Pistoia Alliance has launched the FAIR Implementation project with backing from pharmaceutical companies, including Roche, Astra Zeneca, and Bayer. The FAIR guiding principles – Findable, Accessible, Interoperable, Reusable – were published in 2016 as a collaborative effort among academia, industry, and others. The principles for data management and stewardship place "specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals," the authors write. However, earlier this year, a paper discussed the challenges to implementing the principles. With the launch of its FAIR Implementation project, The Pistoia Alliances hopes to help the industry to undertake "digital transformation" with a toolkit it said will improve data management to enable better artificial intelligence (AI) and machine learning, and support the Lab of the Future.

The Centre of Excellence is organising events, webinars, and hackathons, which will cover a range of challenges aimed at kicking off AI projects or prototype AIs. This will help us to work out where the main challenges are, and to establish an adoption strategy that serves these needs. The Pistoia Alliance's community also provides a virtual and physical co-working space, enabling life science professionals to share best practice and learn how best to apply AI, machine, and deep learning to R&D – wherever they are in the world.

The Pistoia Alliance has released results of a survey that found that 72 per cent of science professionals believe their sector is lagging behind other industries in the development of AI. To accelerate the successful use of AI, The Pistoia Alliance has launched its Centre of Excellence for AI in Life Sciences, aiming to encourage greater collaboration between stakeholders to bridge the gap between technology and science. The aim of the centre is to bring together best practice, adoption strategy, events, and hackathons covering a range of challenges. The survey found adoption of AI is high, with 69 per cent of companies using AI, machine learning, deep learning, and chatbots; an increase from when the same question was asked in September 2017, where 44 per cent of respondents were using or experimenting with AI. This survey shows interest in AI remains strong, but there is still a challenge with moving past the hype to a reality where AI is delivering insights with the power to truly augment researchers' work," commented Dr Steve Arlington, president of The Pistoia Alliance. "It is significant that a majority of people in our own industry believe we are trailing other sectors in the use of AI, and we must address this issue by working closely with each other and with stakeholders in other sectors.

Awareness of Artificial Intelligence (AI), Machine Learning (ML) and Neuro-linguistic Programming (NLP) is ever-increasing, with AI being one of the most talked about technologies across multiple industries. But exactly who is using it? And what are they using it for? The Pistoia Alliance is a not-for-profit organization that aims to promote collaboration between pharmaceutical, technology, and life science professionals to lower barriers to R&D innovation by addressing challenges related to data aggregation and accessibility. One of their current projects focuses on promoting the use of AI in R&D.