Get Ready for Artificial Intelligence in Healthcare

The topic of artificial intelligence (AI) is of great interest worldwide. As all industries undergo a digital transformation, they are finding that the ability to use data is both a competitive advantage and strategic imperative. Healthcare is certainly no exception. In this first part of a two-part blog series, I will discuss some of the technologies that make up artificial intelligence, and in the second post present five use cases for AI technology in healthcare.

The Stage

Within healthcare, as in many industries today, we are seeing a massive digital transformation taking place. A key driver for this is a need to manage costs and improve access against a backdrop of an aging population with increasing prevalence of chronic disease.

Digital solutions like telemedicine, wearables, and remote sensors are opening the door to innovation around patient engagement and preventative care.

And, we’re racing towards a world of precision medicine where genomics and other ‘omics become standard aspects of routine medical care.

These transformations are leading to the creation of massive new data sets, which creates an opportunity for the use of artificial intelligence.

The Definition

Before we go further, let me establish a working definition for artificial intelligence. At Intel, we have developed a taxonomy for artificial intelligence that includes both machine learning and reasoning technologies that enable machines to more closely mimic human capabilities like sensing, reasoning, and acting.

Artificial Intelligence will be a core capability in the digital healthcare enterprise.

Within the area of machine learning, there are two common approaches. The first is classic machine learning that uses statistical techniques like decision trees, random forests, and support vector machines. The second is deep learning, which uses neural networks. A common use for machine learning is classification, which is a common element in predictive models.

Adjacent to machine learning are reasoning systems, both memory, and logic based, which are able to identify patterns across data sets using various techniques and build prescriptive solutions.

These technologies are not mutually exclusive. They each have strengths and weaknesses. Think of them as various tools to solve specific problems. Increasingly we see solutions being developed that involve several of these technologies to solve specific aspects of a broader business need.

The critical precursor for any of these technologies is data. This is why digital transformation in healthcare is so important. Adoption of digital technologies is creating massive new data sets, which presents an opportunity for AI.

Moving Forward

A common question that is posed to me is, “how does an organization get started with AI?”

To answer this question, it’s important to put AI in context with other analytics techniques.

AI is an evolution from traditional analytics. Key capabilities like data governance and training on data-driven decision making set the stage for being able to deploy AI solutions. Critical to this evolution is a top-down commitment from management to use data in order to drive business and clinical processes.

Evolution to AI Requires both technical and organizational data maturity

In the analytics maturity model shown above, you can see that artificial intelligence starts to come into play when organizations transition from using data to analyze what happened in the past, to using data to predict what will happen in the future. Often this transition occurs through the use of classic machine learning to build predictive models from structured data. Naturally, as organizations get more comfortable with this approach, they will look to utilize new, unstructured data sources that open the door for deep learning.

The next step after building predictive models is prescriptive models using cognitive systems that combine the prediction with a business process to recommend a course or courses of action.

In my next post, I’ll dive into use cases for AI in healthcare. In the meantime, what questions about AI in healthcare do you have? Send them my way on Twitter at @andybartley or @IntelHealth.

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Andrew Bartley

About Andrew Bartley

Senior Solutions Architect for the Health & Life Sciences Group at Intel Corporation. Work with providers, payers, life science organizations, and government agencies around the world as a trusted adviser on the development and implementation of leading-edge collaborative care and distributed care solutions. Leverage the latest mobile business client (2-in-1's, tablets, smartphones), Internet of Things, and wearable technologies to deliver superior patient experiences that achieve critical cost, quality, and access goals. Collaborate closely with Intel business and product development teams along with industry partners to define and evangelize standardized architectures that incorporate security best practices and enable the latest data analytics techniques. Regular speaker on the topics of innovation in healthcare and entrepreneurship. Contribute to thought leadership on these topics through the Intel Health & Life Sciences online community. Specialties: Healthcare solution architecture, connected care, medical devices, IoT, wearables, predictive analytics, product and project management, mobile application development, customer journey design, business development, strategic finance, agile software development, lean, SOA, UX, web services, big data solutions, system architecture