AI is one of those technologies that gets hit with hype — maybe even crossing into overhyped. In its early days movies like Terminator and iRobot sparked the worry that sophisticated AI might have the power to become self-aware and subjugate humanity under its iron fist.
More recently, particularly in Silicon Valley, everyone seems to be claiming that their companies and products leverage AI to deliver better services to users. Many of these have begun to undergo harsh critique as investors and consumers ask how sophisticated their technology really is.
The good news is that despite the alarmist predictions and overhyped claims AI is making waves in many industries and in some cases helping make our lives better. Sophisticated AI technologies are powering new levels of innovation in research, programming, and automation, and any industry that deploys them in practical ways stands to see significant gains.
One such industry that’s ripe for AI to disrupt is healthcare. An Accenture report indicated that AI should save healthcare companies as much as $150B by 2026. They also predicted that the annual growth rate for healthcare AI will be a staggering 40% up until 2021.
As an incredibly complex and context-based industry, healthcare can benefit from the use of AI for everything from research to the development of new drugs. Gunjan Bhardwaj, CEO and founder of Innoplexus, an AI and analytics firm explained, “The data challenges facing companies in the life sciences are complex and multidimensional, and therefore require more intelligent systems to support decision making.” As more intelligent systems emerge, problems that used to be unsolvable now seem far less daunting.
Here are the top ways AI will shape the future of healthcare. This will likely be a roadmap for other industries as well:
Research processes are often bogged down in legacy technologies, and inadequate data warehousing. The good news is, AI can streamline the research process by inviting the kind of data researchers are looking for and identifying relevant matches.
As Bhardwaj put it, “Researchers are frequently limited to information they have on-hand, or data stored on whatever platform their organization pays for. The trove of research, patient, and medical data is often unavailable for them, or it’s spread across tens, if not hundreds of sources.” That’s where AI comes in. He continues, “the goal is to bring the wealth of data from countless sources into a singular platform that's easier for researchers to use. This can be done by leveraging the best AI and machine learning technologies, to give professionals access to intelligence that will help them achieve their goals faster and at a lower cost.”
The popular “four V’s of data” no longer represent the complexity associated with processing information. Bhardwaj suggests that companies also consider the depth, density, and diversity of data. These measures help companies do a better job forecasting success, because they help contextualize data based on things like region, demographics, etc.
He elaborates, “Legacy systems and databases rely on known information. They cannot explore unknown information. That is why AI and machine learning are critical components in making data useful and actionable. They help us identify insights beyond what humans can infer from data, like hidden networks, unknown patterns, and undiscovered relationships between biological entities. That kind of intelligence can lead to major discoveries."
Analytics as a Service
The main goal of any organization is to make accessing and consuming data seamless while providing continual insights through intuitive interfaces. In the past, companies treated data, and the analytics derived from that data, as a product to be packaged and sold. This resulted in expensive databases that offered robust analysis of collected data but failed to provide continuing insights. AI is changing that paradigm, however, and AI experts are shifting to an analytics as a service model to help make analytics a constant component of the decision-making process.
Bhardwaj explained, “Insights that make data more useful for supporting decisions were frequently not available on a continual basis, and when they were available, it was often in time-consuming batches that required immense amounts of manual effort. Since health-related industries move at a fast pace, the speed at which you can access and analyze data is critical.” That’s why so many companies within the healthcare space are beginning to turn to analytics solutions that can deliver continuous and current insights.
Faster Drug Development
The research and development process for drugs is generally long, cumbersome, and expensive. This is due to issues ranging from regulatory constraints, down to more practical obstacles like identifying and testing potential treatments. Through the use of AI platforms, however, pharma companies can streamline the development process to deliver a better ROI for any given drug. That could result in increased profits without having to increase prices for patients.
Bhardwaj details, “Pharma companies, doctors, and other health professionals can identify treatments and drugs faster if they tap into existing findings. AI and machine learning give them access to that data, helping speed the pace of innovation." Drugs developed faster is better for the market and for patients, and AI will help pioneer that change of pace in the coming years.
The AI we currently have at our disposal is good at certain tasks and weak with others. AI tends to be strong at crawling data and pattern recognition, while it struggles with more contextualized data. That’s why it has the potential to democratize information for healthcare. Deploying AI can help take massive amounts of data and identify patterns that might be helpful to human experts.
This isn’t only true for healthcare. Every industry has data that is siloed and difficult for humans to evaluate. Through the use of AI, analytics providers are increasing access to insights, and enabling organizations to take action.
As the healthcare industry and others adopt the latest in AI solutions, we can expect there to be an increase in performance and market efficiency. 2018 will be a year where we see a critical mass of industries embracing AI applications, which will help encourage AI companies to continue evolving their services to meet the demand.