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How AI Is Shaping The Future of Life Sciences

The following is a guest post by Gunjan Bhardwaj, CEO and founder of Innoplexus

Noted futurist Alvin Toffler penned a book in 1980 by the title The Third Wave. Toffler’s main argument was that the digital revolution was acting as the next evolutionary step in the line of other great upheavals like the agricultural and industrial revolutions.

Experts in AI are beginning to draw parallels to Toffler’s waves, noting that AI itself may be undergoing a new wave of innovation. They posit that this could, in turn, become the next big revolution to completely change the way we work and live.

While AI hasn’t washed over every industry yet, it has begun to make positive changes in, several industries, one of the most notable being the life sciences. Intelligent machines are increasing access to previously siloed data, and more and more organizations are adopting the latest in AI applications to help accelerate the pace of innovation.

Enhancing Human Intelligence

Ginni Rometti, CEO of IBM, once expressed a compelling position on AI development, “Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.” AI is indeed intended to enhance the human experience, and it’s application in fields like the life sciences, financial markets, and related industries not only amplifies the work of human experts but also improves the quality of life for consumers and end users.

And what better arena to enhance human intelligence than the ones pertaining to our health and well-being? The potential applications of AI are staggering in healthcare and the life sciences, and as such it’s important to consider how these technologies can be used to better the state of the industry today.

Improving the State of Research

One of the most compelling, and beneficial applications of AI and machine learning technology is in medical research. Currently, researchers and medical practitioners are confined to knowledge they possess personally, or that their organization has paid for on unwieldy, outdated systems. As a result, many turn to more generic search solutions like Google to try and find relevant data. These outlets fall short, however, as their algorithms don’t take into account the complexity involved with medical and life sciences research.

That’s why platforms leveraging AI and machine learning, specifically tailored to the needs of researchers are an ideal solution. These tools can help create search alternatives that match the right kind of data to researcher queries. They can also expand a researcher’s information resources to any credible data that’s ever been published.

Democratizing Data Analytics

In the past, quality data and analytics have come with prohibitive price tags, primarily due to the amount of human effort involved in curating and compiling relevant data. As machines become more intelligent, however, we can change the way we look at analytics entirely. When we began building our platform we set out to pioneer a different model, one that offered analytics as a service. By leveraging the capabilities of bleeding edge AI technologies, the amount of human effort involved in delivering data insights is reduced significantly.

This is beneficial for human experts as well, as data professionals can direct their efforts to tasks that are best served by human intelligence. The result is a shift in the way organizations access data insights and a drastic reduction in cost. This means small to midsize organizations can access data that will better support their operations. And with more open access to data, more people will be able to work to solve problems in the field at a faster rate.

Empowering Decision Makers

Whether you’re an executive at a pharmaceutical company, a hospital CEO, or a research leader, you need the most current, relevant, and contextual data at your disposal to make the best decisions possible. As AI democratizing access to this data, it’s becoming easier for key decision makers to leverage continuous analytics. Gone are the days of spending significant portions of budgets on data aggregated from last year.

AI is making it possible to crawl the endless sources of information out there and provide real-time analytics for organizations. Also, machine learning technologies are helping create more accurate predictive forecasts, which can help in a number of ways. Pharma companies can more accurately predict how successful a drug will be in particular markets. Researchers can make queries that intelligent machines can connect to previously unrealized insights. And any organization, whether in the life sciences or not, can benefit from more sound business forecasts based on better data.

Riding the Wave of AI Disruption

As AI and machine learning technologies mature, adoption is increasing across all sectors. What does this mean for organizations in industries being disrupted by AI? For most, it means that AI should be a central part of growth strategies, particularly as competitors begin to deploy AI to improve their operations.

It also means that organizations should think of areas in which they’d benefit from enhanced intelligence. In doing so, they can identify what business functions can be best supported by tech upgrades, while having a better idea of what their ROI will be. As these technologies disrupt the way we approach problems in these industries, we can expect a rapidly increased rate of innovation that will eventually be recognized as nothing short of a revolution.