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A recent report by Deloitte projects that the artificial intelligence (AI) drug discovery market will grow 53% by 2025, citing the increased investment of labs in AI as a way to boost research but also to collect, analyze, and produce real-time, accurate data. Of course, such information, including when also applied to the health outcomes side to demonstrate drug value—whether we call it real-world evidence, real-world data, predictive modeling/forecasting, etc.—has been, at different levels, part of broader pharma practice for several years now, as the industry, some might say, begrudgingly compelled itself to get on the digital health train.
But pharma and its partners have taken advantage, as we examine in our December issue, focusing on strategies to build and sustain. Yes, the industry has its long-rooted regulations and risk assessments and compliance and data-protection measures, that in the delicate world of medicine, you can’t as easily adapt to next-gen trends and technology as other enterprises; but who can really ignore the strides made today in advanced analytics and digital health? Tools such as natural language processing, data and text mining, data visualization, wearables, system checkers, and so on—and the opportunities they offer in clinical operations, patient and physician engagement and support, and also in health economics and the market access/reimbursement realm.
Which brings us to Covid-19 and how this whole notion of data integration has been reframed. Edward Hensley of AssistRx points out in his guest feature this month that Covid sparked the acceleration of technological change and adoption, as daily routines were altered. A change he says was already afoot but now is requiring, out of necessity, the mass use of existing digital tools. A survey by Optum supports this, finding that 56% of healthcare executives said their companies accelerated AI plans in response to Covid. Generally, however, it seems progress remains in embracing AI. In a survey by MMG, doctors gave a score of 4.65 out of 10 to the level of development of AI; pharma/healthcare executives rate it at 4.79, and medical students at 5.11.
Perhaps acceptance simply comes down to trust, as Aktana’s Derek Choy explores in our December coverage, outlining ways he says AI 2.0, or “explainable AI,” can actually build trust between user and machine. He argues that the complexities of the life sciences “demands more humanity in modern AI.” Doesn’t sound too unreasonable to consider a little bit of “new” in these not-normal times.
Mike Christel is Editorial Director of Pharmaceutical Commerce. He can be reached at email@example.com