How AI is Revolutionizing Healthcare

To understand the basic premise of AI, you have to understand how the human brain works. All of our intelligence and knowledge that humanity has accumulated over millennia resides in each of us. For example, we take wine for granted, but it never occurs to most people that making wine is not a simple process. Someone first came up with the idea, then it took thousands of years for this knowledge to be perfected and spread. And now you can learn how to make your own wine in a matter of days.

This basic concept of accumulated knowledge applies to artificial intelligence. “Learning” and “problem-solving” are core definitive parts of the term “AI”, because intelligence is not something that is there by default; it is an acquired state.

The aforementioned fits perfectly into the realm of healthcare, as it requires an enormous amount of accumulated knowledge due to the complexity of the subject matter and the systems in question. Diagnosing someone today is not just about measuring their temperature and asking them questions. A wide variety of modern medical tests examine the individual on a molecular level.

Another great property of the subject matter is the fact that the examined system is stable. Meaning that the human body is predictable to a certain extent. If you eat a lot, you get fat. If you drink a lot, you get liver problems. The level of prediction varies as more variables are introduced.

However, what’s important is that the backlog of data available for analysis is astonishing and it deals with the same framework (the human body). This information can be used to make accurate healthcare predictions, using AI. It’s not the stock market, where some situations are impossible to predict. All of this information is what feeds AI-based technologies that work in healthcare and this same information is why healthcare sees a wide variety of AI applications being developed all the time.

Now let’s take a closer look at why and exactly how AI is shaping the medical world of tomorrow.

Healthcare is a Dream for a Data Freak

When we talk about tracking, collecting and analyzing data – healthcare is probably on top of the list, along with weather data and other limited types of preserved and multiplied records. For example, one current study within the British healthcare system turned 70 just a couple of months ago. That’s right. Someone’s been carefully collecting various datasets about a large group of people throughout their lifetimes.

And this is also what Google’s Deep Mind Health is doing. It’s mining millions of healthcare records to learn and build predictive models around specific diseases and health conditions. From cancer research to diabetic retinopathy – disease propensities will soon be easily tackled with artificial intelligence.

Healthcare Speaks the Same Language

Machine learning is an integral part of artificial intelligence: it is the methodology and technique which the ‘artificial’ uses to acquire the ‘intelligence’. Machine learning comes in different forms, but one of the main languages currently championing this AI domain is R.

What’s particular about R is that it was developed for statistics applications. It was made to deal with large sets of data. That’s why it’s being actively used in clinical research. And this is exactly why R is also popular among data scientists, who happen to pioneer the machine learning landscape, as machine learning makes their job a lot easier. Given that all of these medical researchers already know the ins and outs of R, their job of transferring these concepts into the realm of artificial intelligence is a lot easier.

It’s Not Always About Altruism


It’s important to note that AI technologies in healthcare are not always being propagated by the sole desire to make lives better. There is money to be made and, in many cases, to be saved.

In 4 years, the AI market in healthcare is projected to reach 6 billion dollars. If that doesn’t look like much, how about the American healthcare insurance market? This market is currently valued at more than $800 billion. That’s roughly 25% of the US Federal Budget for 2017. It’s also the same amount of money the US spends on the military per year. Impressive, right?

All of these insurance companies are in it for the big bucks. The better they predict health risks, the more precise their underwriting will become. Underwriting is the process of calculating risk and individual propensities for each insured person with the goal of identifying the right insurance coverage (how much should they pay, etc.). And that’s why machine learning is already being used for various insurance applications, from risk assessment and preventive care to claims management and fraud detection. Insurance companies need healthy people. As they want to spend as little money as possible to keep these people healthy. That’s why preventive care is a very hot topic within the insuretech world.

The Data Potential of Healthcare Is Unlimited

While you can’t go beyond dollars in the financial world, you can, however, go beyond disease symptoms within the healthcare industry. There are molecules, proteins, DNA sequences and much more to consider and investigate. Any of these specific levels of organic life could be potentially subjected to artificial intelligence applications and healthcare research tools. Even Twitter serves as a data source for healthcare initiatives.

However, there are far more ambitious tools and applications. Like trying to cure cancer by analyzing our genetic code. Viral genome classification is also an important healthcare application, where AI can play its crucial role.

This is the final frontier for humanity within healthcare. Currently, we can’t go any further simply because we don’t have the neural capacity to process all of this information in a meaningful way. However, eventually this will change as even on the basic level, AI is slowly taking over humanity’s home turf. Radiology is a good example of a healthcare application, where AI is progressing at a steady pace, with computer programs getting better at CT scans and other imaging applications.


Healthcare is one of the most promising and lucrative applications for artificial intelligence. Health institutions want to cut costs by lowering readmission rates, and insurance companies want to optimize their risk management techniques, while pharmacological companies want to cure viruses. All of these and many other AI applications are already being actively explored by a wide variety of healthcare and healthtech companies around the world.

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