Life Sciences: The New Frontier in AI Disruption
Utilisation of artificial intelligence (AI) in life sciences is on the cusp of rapid expansion as it transforms research, analysis and delivery of healthcare by bringing major changes to the way multiple fields operate.
In terms of global market size, AI in life sciences is forecast to grow nearly 30% annually to reach $12.7 billion by 2030, according to Straits Research. Though the term is commonly used and its implications frequently discussed, AI is not easy to strictly define. It is commonly defined as the ability of digital devices to think and carry out processes or tasks in a similar way to humans. What distinguishes AI from a computer or machine running on a simple programme is the ability to learn from new information, hence the name for one of the crucial elements of the new technology: machine learning.
Straits Research (2022)
Intelligent life and the sciences
AI is already being deployed in a variety of ways in life sciences, and its use set to grow in both scale and scope.
Early in the pandemic, Pfizer and Sanofi used AI and machine learning to analyse vast datasets to help expedite the rapid development and testing of their vaccines. Pfizer has also been employing AI to assist in its screening of millions of potential compounds in its search for an effective oral medication to treat coronavirus infections.
The R&D process for medications is generally longer and costlier than that for vaccines, often running into billions of dollars and taking around a decade for the entire research-to-market cycle. Adding to the costs and risk is the fact that a majority of potential new treatments are found to be ineffective or never receive regulatory approval.
Smarter and faster
AI can help to increase efficiency through the enhanced screening process, as well as by better design and analysis of clinical trials. This includes avoiding repetition of previous studies through automated analysis, creating more patient-centric trials that reduce dropout rates, more diverse patient ranges in trials, AI-powered wearables making remote trials more practical, and smarter analysis of results that takes into account data from a wider range of sources.
The leveraging of big data analysis also has a myriad of applications outside of clinical trials, such as in genome sequencing, patient diagnosis, personalised healthcare and the modelling and tracking of new diseases. The integration of data mined from field research, laboratories, clinical settings, wearables and even sources like social media, will provide scientists with new and more targeted methods of inquiry and development, as well as make better and faster decisions.
Given the substantial overlap between many of these areas, the use of AI will for certain interact across them, likely in ways that have yet to be seen. And perhaps in some ways that are discovered in the future by AI itself.
More intelligent devices
Combining AI with technology is another element that will drive advances in life sciences. As well as wearables and diagnostic technology, fields such as telemedicine and robot surgery will also reap the benefits of the ongoing advances in AI.
In the case of telemedicine, in addition to being able to consult with a health professional remotely, AI offers up the possibility of enhanced diagnosis and monitoring through wearables, as well as data-driven analysis being delivered to both parties in real-time during the consultation. Similarly, an AI-driven system could provide real-time analysis and data to assist both the human and robot surgeon during an operation.
Changing the business
On the administrative and commercial side of the life sciences, AI is on its way to causing the same levels of disruption and transformation that it has begun triggering in other fields.
The kinds of efficiencies that AI and machine learning can bring to processes such as maintaining patient records, office administration, billing and the archiving and accessing of research are easy to imagine. But they also have the potential to transform areas that include marketing, sales and customer experience. Some of this is already underway in other commercial sectors, but life sciences companies and organisations are widely acknowledged as being somewhat behind the curve in their deployment and leveraging of AI’s potential across the board.
For better or for worse, a future in which individually-targeted marketing advertises the benefits of personally-tailored healthcare at the precise moment when the potential patient is at their most emotionally receptive to such a pitch is no longer the stuff of science fiction.
As with the related technology of digital transformation (DX), Japan has not been at the forefront of AI implementation in healthcare or the wider life sciences. But change is coming and along with it the need for personnel with relevant expertise in AI’s deployment and application in its many forms.
The Ministry of Economy, Trade and Industry forecasts the shortage of IT staff overall in Japan will be in the range of 450,000 to 790,000 people by 2030. Whatever proportion of that shortfall turns out to be in life sciences, the impact on its continued rollout in those areas looks set to be significant.
Increased demand is already evident in Japan for AI-savvy staff in positions from pharmaceuticals R&D – especially around protein structure and biomarkers – through to business intelligence and data science roles on the commercial side.
While AI can help alleviate the labour and skill shortages that Japan is experiencing in multiple sectors and fields, the need for personnel with relevant expertise in its deployment and application in its many forms will grow.
By: Gavin Blair
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