AI Sector Overviews Articles and Reports
Artificial intelligence “sector overview” reports are designed to help business leaders explore the possibilities and important AI trends across industries. Search our sector overview reports below:
When it comes to effectiveness of machine learning, more data almost always yields better results—and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators.
Where does all this data come from? If we could look at labeled data streams, we might see research and development (R&D); physicians and clinics; patients; caregivers; etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments. Hence, the present-day core issue at the intersection of machine learning and healthcare: finding ways to effectively collect and use lots of different types of data for better analysis, prevention, and treatment of individuals.
Burgeoning applications of ML in pharma and medicine are glimmers of a potential future in which synchronicity of data, analysis, and innovation are an everyday reality.
At Emerj, the AI Research and Advisory Company, we research how AI is impacting the pharmaceutical industry as part of our AI Opportunity Landscape service. Global pharma companies use AI Opportunity Landscapes to find out where AI fits at their company and which AI applications are driving value in the industry.
In this article, we use insights from our research to provide a breakdown of several of the pioneering applications of AI in pharma and areas for continued innovation.
Though yet to become a standard in schools, artificial intelligence in education has been taught since AI's uptick in the 1980s. In many ways, the two seem made for each other. We use education as a means to develop minds capable of expanding and leveraging the knowledge pool, while AI provides tools for developing a more accurate and detailed picture of how the human mind works.
There is a certain level of stigma that exists around using machine learning and location data in business applications, understandably due to risks inherent in exploitation of individual privacy. But if we look under the hood of society's daily web of interactions, we see that the location information economy—from GPS to radio signal based-triangulation to geo-tagged images and beyond—is now almost ubiquitous, from the moment we track our morning commute to the end-of-day search for healthy and convenient take-out for dinner.
Human resources has been slower to come to the table with machine learning and artificial intelligence than other fields—marketing, communications, even health care. But the value of machine learning in human resources can now be measured, thanks to advances in algorithms that can predict employee attrition, for example, or deep learning neural networks that are edging toward more transparent reasoning in showing why a particular result or conclusion was made.
With all the excitement and hype about AI that’s “just around the corner”—self-driving cars, instant machine translation, etc.—it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you're already using—right now?
In virtual worlds, AIs are getting smarter. The earliest instance of artificial intelligence in games was in 1952, when a lone graduate student in the UK created a rules-based AI that could play a perfect game of tic-tac-toe. Today, teams of researchers are working on—or have already succeeded in—creating AIs that can defeat humans in increasingly complex games.
Executives worry about their businesses.
They often have to navigate, with limited resources, a stormy market made of customers, competitors, and regulators, and the interactions between all these actors make finding answers to business questions a complex process.
In 1895, the German physicist, Wilhelm Röntgen, showed his wife Anna an X-ray of her hand. “I have seen my death,” she said. Medical imaging broke paradigms when it first began more than 100 years ago, and deep learning medical applications that have evolved over the past few years seem poised to once again take us beyond our current reality and open up new possibilities in the field.