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What Impact Will Artificial Intelligence (AI) Have on Clinical Trials?
Managing Director, North America Connected Health Lead, Accenture
Andy Greenberg is an expert in launching technology-based products in the life sciences, healthcare, and wellness spaces. With 15+ years leading and developing companies in start-up environments, to 4+ years as a consultant supporting digital strategies for pharmaceutical companies around the world, Andy brings an in-depth knowledge of behavioral research and the latest in AI, sensor, and mobile advancements, to help organizations accelerate their success.
Despite the potential Artificial Intelligence (AI) holds to all but revolutionize healthcare, pharma companies can be reticent to embrace the technology. This hesitancy is understandable given AI’s nascence, the high risks of potentially costly (and even deadly) errors, and historic reluctance of regulatory agencies surrounding new technologies. However, pharma companies that don’t begin incorporating AI into their clinical trials will miss opportunities to reduce costs, glean new insights, produce new therapies, and move to market more quickly, among other benefits. Indeed, from discovering new molecules to improving the quality of life for patients on therapy, AI stands to increase the broader medical industry’s value by about $150 billion over the next seven years.*
In a somewhat paradoxical twist, the traditional barriers to adopting new technologies in clinical trials – namely, regulatory roadblocks – are not the things delaying pharma companies’ entry into AI-enabled clinical trials. Instead, it can sometimes be the conservative approaches of their own internal regulatory teams that can block the path to the adoption of disruptive technology. Put another way, to a large extent, big pharma companies sometimes stand in their own way when it comes to utilizing AI.
In fact, the U.S. Food and Drug Administration (FDA) recently issued a proposal seeking input on how to best incorporate emerging technologies such as AI into drug manufacturing and medical discovery. The FDA said it is “considering a total product lifecycle-based regulatory framework for these technologies that would allow for modifications to be made from real-world learning and adaptation, while still ensuring that the safety and effectiveness of the software as a medical device is maintained.”
“Beyond missing out on the benefits of faster and more efficient drug discoveries and speed to market, pharma companies slow to adopt AI can risk losing market share to more nimble startups.”
What’s at stake
Beyond missing out on the benefits of faster and more efficient drug discoveries and speed to market, pharma companies slow to adopt AI can risk losing market share to more nimble startups. The FDA’s recent announcement that it has not yet taken a position on newer technologies is further proof that the market is ripe for disruption. At a minimum, big pharma should consider partnering with smaller, more tech-savvy startups to get the AI ball rolling.
For example, last year, Roche announced its acquisition of Flatiron Health, a healthcare technology and services company focused on accelerating cancer research and improving patient care. Founded in 2012, part of Flatiron’s mission is to break down the silos across health systems. Specifically, the company’s aim is to collect, synthesize and utilize the unstructured data “stored across thousands of disconnected community clinics, medical centers and hospitals.” Startups such as Flatiron are part of the industry dynamic that Accenture calls New Science –an evolving, unique combination of the best in science and health technology (e.g., genomics, biomarkers, companion technologies, delivery methods, etc.) that is filling an unmet need and raising the standard of care.
Another example is BenevolentAI, a 250-person company founded in 2013 that pores over massive quantities of scientific data to significantly increase the speed at which drugs are developed for rare diseases. The company recently announced a deal with Parkinson’s UK and the Cure Parkinson’s Trust to utilize BenevolentAI’s “healthcare knowledge graph,” which comprises more than 1.3bn bioscience relationships and is likely to lead to breakthrough treatments.
Today, most big pharma companies lack the agility of health startups such as Flatiron and BenevolentAI. But partnering with these – and other – companies is one way for industry stalwarts to make inroads toward greater outcomes through technology. At a minimum, they can start demonstrating the value of AI in areas viewed as lower risk to the organization because they don’t directly “touch” the patient. These can include the application of Natural Language Processing (NLP) in search within the company’s own data or conversational AI for improving the site investigator experience. Pharma companies need to understand that as AI increasingly reaches new levels of sophistication, AI will become virtually indispensable, far beyond its function of processing massive amounts of data.
For an industry as highly regulated as life sciences, AI offers new opportunities to address crucial life-and-death issues more quickly and cost-effectively. Companies already seizing the AI moment and looking over the horizon toward new and potential use cases face less risk in the long-run and won’t struggle like slow adopters to catch up with new regulations and patient expectations.
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