Developing drugs is like searching for a needle in the proverbial haystack, and that’s just the beginning of the process.

So writes Jackie Hunter, CE of Clinical and Strategic Partnerships at London-based Benevolent AI, in a recent commentary on how AI is changing drug research.

Casting the problem in a nutshell, she says the pharmaceutical has not been innovating in a socially productive way.  “Currently, most drugs fail in phase II and III clinical trials because we aren’t actually modulating the right target in the disease. Sifting through clinical drug reports is often like looking for a needle in a haystack; trials can include several hundred patients and generate reports 26,000 pages long. You can’t effectively analyze that data with any degree of granularity by hand.”

The good news, she says, is that AI has to potential to flip the problem “on its head; with machine learning, vast sums of data can help to improve the quality of the result. We can train systems to recognize and select several high-quality ‘needles’ out of that haystack and focus our resources on following those leads, rather than scrabbling around manually to find just one. AI gives us rapid access to more relevant data for the disease and helps us to understand its biology – and ours – more comprehensively. Increasingly, AI is pivotal to maximizing the benefit of the available genetic or genomic information we accrue, as well as for analyzing existing electronic medical records.”

The upshot stands to be transformational in terms of both time and cost. Indeed, she notes, “artificial intelligence permeates nearly every industry today and its application in healthcare is the only way that the vast amount of medical data being generated around the world will be fully utilized.”

That said, she points also to the continuing challenges related to AI development – particularly the issue of data privacy and how, in her view, it isn’t being adequately – while noting as well the danger in developing “medical solutions in silos.  . . . By creating solutions in a bubble, or by focusing too closely on one area – be it the industry, the healthcare provider, or the patient – we risk overlooking key data and either creating new problems for people, or excluding entire swathes of the population from effective treatments. It has to be an integrated discussion about that whole healthcare data ecosystem.”

And when that discussion is in place and effectively guiding the use of new AI technologies, she believes, the result will be “more effective medicines to more people more quickly, at a fraction of the cost and resources.”