Life scientists lead AI charge, but one quarter face data obstacles
Adoption of artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) is well advanced in the life sciences sector, with 44% already using or experimenting with these technologies. But issues with access to data and data quality are the biggest barriers to wider adoption of AI, according to 24% and 26% respectively, with 26% and 19% citing the same problems around ML and NLP.
A survey by The Pistoia Alliance, a global, not for profit alliance that works to lower barriers to innovation in life sciences research and development, among 374 life sciences professionals, revealed that the use of AI to augment human intelligence in this sector is well advance, but facing problems familiar to the commercial sector. Life sciences and pharmaceutical research and development are data-rich, especially with the rise of health trackers and connected sensors, which makes techniques like ML and NLP essential. But if access is limited or data quality poor, positive outcomes will be constrained.
“AI has the potential to revolutionise life sciences and healthcare - all the way from early pre-clinical drug discovery to selecting precision treatments for individual patients,” said Dr Steve Arlington, president of The Pistoia Alliance.
“Our survey data shows that, while life science professionals are already exploring how AI, ML and NLP can be used, there are clear gaps in the knowledge, data, and skills which will enable more pharma and bio-tech companies to achieve tangible results from AI. Impediments to success, such as a lack of industry-wide standards for data format, will need to be addressed, if the potential of AI and ML is to be realised. We urge those in the pharmaceutical, biotechnology and technology industries to explore ways in which they can collaborate now, to find answers to common problems of the future,” he said.
One recent initiative that may help was the release by Elsevier of its Unified Data Model as open source for members of the Pistoia Alliance. But while nearly half of researchers are ahead of the curve with these technologies, 30% of respondents in the survey admitted they are not using ML, 27% are making no use of NLP and 11% are not involved with AI. A further 8% admitted that they know next to nothing about AI and deep learning.