What does the future of machine learning look like, and how is this relevant to healthcare?

When we picture the landscape of artificial intelligence ten or fifteen years from now, perhaps our minds don’t immediately direct themselves towards the healthcare industry. It’s relatively easy for us to imagine eerily lifelike robots roaming around, or automated machines replacing humans in dangerous or unpleasant situations – but there are a huge number of far more realistic (and slightly less unnerving) opportunities that AI allows us to explore.

The field of natural language processing holds a lot of potential in terms of helping the healthcare sector better understand patient needs and desires. By taking the time to listen to what patients are saying online about their particular course of treatment, data analysts are able to sort through mountains of raw data and condense it into digestible, understandable material that drug researchers and developers can actually use.

Of course, when you’re dealing with the huge amounts of information necessary to obtain valid and consistent results, it helps to have machine learning on your side. By automating the analytics process to a certain extent, data analysts are able to perform their tasks more quickly and efficiently – providing biotech companies with the information they need much, much faster than would otherwise be expected.

Allowing systems to learn, deliver and improve unsupervised frees up a lot of time, which research and development teams can use to focus on what matters most: developing better and more effective treatments for some of the most stubborn and challenging medical conditions. With only one in ten drugs making it through the clinical trial phase and onto the market, biotech companies are taking a big – and often very, very expensive – gamble. Providing researchers with accurate data quickly speeds up the entire process whilst making it more accurate and reliable, potentially bringing life-changing medications to patients far faster than would otherwise have been possible.

Using AI methods such as natural language processing and data analytics in this particular way opens up a world of more compassionate healthcare, where patient needs, thoughts, and opinions are front and center. If the past eighteen months have taught us anything, it’s that there’s no one-size-fits-all approach to care, with each individual patient having a set of requirements that is unique to them. A combination of data analytics and machine learning is making this detailed level of understanding possible, resulting in a faster, more efficient, and more empathetic drug development process.

Machine learning architectures are being leveraged by a growing number of organizations within the healthcare industry in order to provide a better patient experience. Of course, there’s no telling where exactly AI will lead the patient care sector, but we’re already making breakthroughs that we hadn’t predicted, and we’re excited to see where we end up.

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