Artificial Intelligence and Machine Learning are powering incredible changes across a huge range of industries. But in data and research-dependent industries such as pharmaceuticals, they’re having an unparalleled impact. From improving candidate selection processes for clinical trials, to accelerating new drug development, AI is quickly becoming an essential tool for those that want to stay competitive in this dynamic industry.
Modern data management and analytics have completely transformed the global pharmaceutical industry. It’s a field that’s been ruled by data from day one, and new ways of managing and extracting value from clinical data are helping both major incumbent players and agile new competitors achieve amazing things.
Where big data and analytics laid the foundations for a smarter, faster, and better-informed pharmaceutical industry, AI and Machine Learning are now taking things to the next level – going beyond offering simple insights by providing truly proactive optimisations for numerous pharmaceutical processes.
In a data-rich field like pharmaceutical research and development, the potential applications of AI are almost limitless. However, the improvement of clinical trials has quickly emerged as one of the most exciting and promising use cases – demonstrating clear and immediate value for applying AI in the industry.
AI is currently helping pharmaceutical companies improve clinical trials in three major ways:
- Improving patient recruitment
In 2018, Mayo Clinic reported that IBM Watson had helped improve clinical trial enrolment by 80% by better matching patients to trials based on specific criteria. By quickly analysing patients from broad pools and identifying the best patients for a given trial, AI helps ensure uptake by providing trial opportunities to the most suitable candidates.
- Optimising trial design
Many companies are exploring new ways to apply machine learning algorithms to clinical trial workflows and enable continuous operational improvement. By constantly analysing workflows in granular detail, machine learning is helping pharmaceutical companies identify and iron out inefficiencies in their clinical trial processes – making them faster and more cost-effective.
- Trial output optimisation
By offering pharmaceutical teams deeper insight into the patients involved in their clinical trials, AI is also helping to solve some of the oldest and most persistent challenges in clinical trial administration. AI is helping teams proactively identify when a patient may be about to stop engaging with a trial and drop out, and act on that insight before the validity of the trial is put in jeopardy.
Enabling predictive power in the R&D process
While AI clearly has powerful applications within clinical trials, perhaps the most exciting and biggest change that it’s bringing to the pharmaceutical industry is that in many cases, it’s cutting the number of trials that need to be completed to reach a meaningful conclusion.
By accurately predicting how drugs will interact with trial patients, AI can effectively reduce the total number of clinical trials by up to 70%. Essentially, the analysis and insight offered by AI helps remove some of the random elements that hinder clinical trials, reducing the need to compensate for those factors with a larger trial group.
However, it’s worth noting that evidence of this impact is still relatively thin on the ground. Because so many different costs and processes go into research and development, it’s difficult to measure the exact impact that applying AI within a couple of those places is having on overall outcomes.
What we do know however is that AI is extremely good at making sense of huge volumes of diverse data. In many ways, this is the piece that’s been missing from pharmaceutical research and development.
By understanding and processing new clinical research, identifying patterns in clinical trial data, and looking at structured and unstructured pharmaceutical data alongside one another, AI can help pharmaceutical research and development teams get more from their data – and gain greater understanding of the context that data exists within – than ever before.
Plus, it also has an important role to play in the complete digital modelling of patients. By constructing a complete ‘digital you’ using new technologies, pharmaceutical and healthcare teams can better predict and model the potential impacts of different drugs and effectively conduct entirely digital trials. Although, we’re still quite a way away from those trials being a complete replacement for patient-based clinical trials.
Adoption is high – and growing
Despite such clear benefits to those using AI and Machine Learning, most industry experts believe it will still be 10 years before AI is fully integrated into the R&D department of biotechnology and pharmaceutical companies. Within this time, it is anticipated there will be some important AI-related drug products developed and approved for use.
As the availability of incredibly powerful open source AI platforms such as IBM Watson increases, more and more companies will experiment with the capabilities they offer. And as more companies experiment with AI platforms, inevitably, more powerful applications for the technology will emerge.
Even if the current major applications of AI – such as improving the efficiency of clinical trials and accelerating drug development – were the only ones to exist, we’d still see AI become a mainstay across the pharmaceutical industry over the next few years. However, what’s really exciting for those in the pharma field is that the best may still be yet to come.
While powerful, it’s important to remember that AI is a fledgling technology, and as more pharmaceutical companies get to grips with it, it’s extremely likely to evolve far beyond what we expect of it today.
By 2021, companies are expected to be investing around $6.6 billion in AI – and healthcare is one of the biggest areas of growth. If as much changes in the next three to five years as had shifted between 2014 and 2019, the technology’s impact on pharmaceutical research and development will be profound, and nothing short of transformational.
For companies that haven’t yet invested in the technology, the message is clear: AI is already helping your competitors achieve amazing things, conduct fewer trials, and develop new drugs faster. If you want to keep pace with them – both now and in the future – it’s a technology you simply can’t afford to overlook.
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