Is Healthcare the Next AI Battleground

On Sunday, 60 Minutes dedicated half of its program to discuss the rapid evolution of artificial intelligence(AI). Most of the interviews and case studies presented were related to the health care industry which clearly illustrates how that industry is becoming one of the key areas of focus for AI technologies.

Vertical solutions are called to pay a pivotal role in the next phase of AI. While the initial phase of the AI market has been centered on general purpose platforms, the focus has been slowly shifting towards industry-specific solutions. Among those industries, healthcare has been a very early adopter of AI technologies and has helped to push the innovation in the space.

In recent weeks, we’ve seen announcements from IBM and Google about large investments to leverage their Watson and DeepMind technologies respectively to assist oncologists and researches in the fight against cancer. For obvious reason, curing cancer is a very high profile healthcare problem which is likely to attract many headlines. However, the healthcare industry is full of challenging scenarios that can immediately benefit from AI and cognitive computing techniques. From improving medical research to optimizing diagnosis, AI can play a pivotal role in the next phase of medical technology solutions.

What Makes Healthcare An Exciting Market for AI Platforms?

There are a few factors that make healthcare a prototypical market for many AI capabilities. Let’s explore a few ideas:

Gathering Insights from Unstructured Healthcare Data

Most of healthcare data can be considered “black data” in the sense that it can’t be easily analyzed. There is a world of knowledge accumulated in physician notes, images, voice, session recordings and many other data sources that are incredibly hard to analyze using traditional BI methods. AI algorithms that can process text, speech, audio and vision can be very effective extracting knowledge from these data sources.

Improving Diagnosis Processes

Modern AI platforms already have the capability of learning from the knowledge of top experts on a specific medical field. That knowledge is typically accumulated in the form publications in medical journals or in historical diagnosis records. Using Training AI algorithms to acquire that knowledge and applying to combining it with Doctor’s expertise can drastically improve the effectiveness of a specific diagnosis techniques.

Improving Medical Research

Modern medical research leverage vast amounts of data from heterogeneous data sources. In many scenarios, the empirical analysis techniques used by traditional BI platforms is simply insufficient to have an impact in medical research processes. AI models can exponentially increase the levels of intelligence obtained from those data sources that can directly be applied to medical research initiatives.

Combining Digital and Human Knowledge

We are entering the golden era of medical device technology. These days, patients can have small devices implanted in their bodies that are constantly gathering data relevant to specific set of medical conditions. While these devices are mostly use as data recorders today they will soon incorporate AI techniques to intelligently interpret the collected data and make appropriate decisions based on the training provided by experts on that medical field. From that perspective, AI will act as a sort of bridge between digital data and human knowledge.

CEO of IntoTheBlock, Chief Scientist at Invector Labs, I write The Sequence Newsletter, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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