Artificial intelligence: continuing to revolutionise the life sciences value chain
This article was co-authored with Jenny Yu, Chemical & Life Sciences Practice Leader for the UK and Ireland at Marsh. As this is a fast moving topic, please note that this article is current as at 22/03/2022. For further information, please contact Paula Margolis, Samantha Silver, or Jenny Yu.
Artificial intelligence (AI) has transformed the life sciences industry in ways that we could never have imagined. It now impacts each stage of a life science product’s lifecycle — from research and drug discovery to clinical trials, manufacturing, supply chain logistics, marketing, and sales.
Recognising the potential benefits, industry leaders across the world have increased investment in AI, with the global AI healthcare market expected to grow from US$4.9 billion in 2020 to US$45.2 billion by 2026.
The COVID-19 pandemic has further intensified focus on the application of AI across the entire value chain. While companies from all industries adapted to the 'new normal' of daily remote collaboration and AI technologies, life science companies also leaned more heavily into the use of AI in their mainstay work of drug and medicine development and marketing.
The development and bringing to market of COVID-19 vaccines in less than a year is testament to the power of AI, as well as the remarkable collaboration of industry stakeholders.
Post-pandemic, there is no doubt that the industry is primed to harness new ways of working with new technologies. However, with these opportunities comes new risks. The increased use and application of AI in life sciences inevitably raises questions as to its impact on the legal and regulatory risk landscape.
The application of AI in life sciences
As one of the most highly regulated industry sectors in which there is a particularly close relationship between product safety and health outcomes, life sciences has traditionally, and understandably, approached the adoption of new AI-driven technologies with a large degree of caution. In spite of this, the sector has now embraced the innovation due to the wealth of opportunities for AI application. Companies are beginning to use AI to automate existing processes across the entire life sciences value chain, including the following:
- The diagnosis and identification of diseases, such as cancers and nervous system disorders. Researchers from Cambridge and the Alan Turing Institute have developed an AI system that can diagnose dementia after a single brain scan.
- The discovery and validation of new drugs. UK scientists have used an AI-powered drug discovery platform to identify cancer treatment drugs that could be used to target a specific gene mutation in children with incurable brain cancer.
- Testing the quality, efficacy, and safety of new products more quickly. The FDA is using AI tools to develop models that can predict adverse events for new drugs by examining available post-market surveillance data for existing, marketed drugs.
- Conducting clinical trials by, for example, using AI technologies to collate, consolidate, and analyse clinical trial data at speed.
- Developing personalised and precision medicine. Deep learning algorithms can identify genome sequences.
- Identifying manufacturing issues, including regulatory bottlenecks with greater speed and accuracy.
- Designing robust and more responsive supply chains, including forecasting demand and supply enabling manufacturers to accurately manage production levels, and increasing visibility and traceability.
Owing to the industry’s adoption of AI, it has been at the forefront of developing mechanisms to deal with any potential negative impacts that may arise from its use, such as cybersecurity protections. This reflects a general trend where new technologies are often being used and regulated first within the life sciences sector with other sectors, such as consumer products, following suit.
In the next article of our Artificial Intelligence in Life Sciences – Revolution, Risk, and Regulation series, we will cover some of the key risks associated with the use of AI in life sciences.