Industry leaders that seemed unable to fail are finding it hard to survive without keeping up with technological trends. Brands like Blockbuster, Borders and KODAK faltered due to innovation lag. Unfortunately, falling behind is even more consequential within certain sectors. This is why medical professionals and organizations must pay attention to machine learning in healthcare.
Fortunately, this technology has already made its way into the medical field. Many practices use some form of artificial intelligence (AI) without even knowing it when they utilize specific proprietary solutions.
Machine learning in healthcare is now commonplace, but what exactly does this technology entail? Most people have seen it mentioned in the media and academia as a tool to improve organizational, practitioner, and patient outcomes. These results are achieved because machine learning is a form of artificial intelligence that utilizes data and algorithms to imitate human learning.
This sounds like a straightforward definition, but the technology itself is extremely complex. So much so that it has become a central aspect in the field of data science. Imagine a system that can analyze data from millions of open-heart surgeries in order to pinpoint a specific patient’s unforeseen risk factors. This is just one example of machine learning in healthcare.
Some say this technology is an alternative to human learning, but in reality, it’s an improvement. Machine learning allows programs to learn on their own and improve over time. We’ve seen the tech surpass human thinking in areas ranging from gaming to — ironically — creating artificial intelligence. When a process can improve outcomes, it’s irresponsible not to take advantage.
It’s one thing to point out an example of artificial intelligence assisting in healthcare. It’s quite another, however, to show why it’s necessary for the medical field. The model of machine learning for open-heart surgeries certainly stands out. But haven’t countless skilled surgeons performed this procedure without the assistance of AI?
The answer is yes, but more issues showcase how machine learning in healthcare can excel the field to new heights:
Did you know that countless productive hours are lost every day in the medical field due to mundane yet necessary tasks? This affects every area of healthcare. For instance, medical coders spend about 25% of their time handling “repetitive, structured and standardized” tasks.Machine learning allows for automating such tasks — letting professionals focus on more pressing matters.
Machine learning in healthcare also makes the time spent with patients more productive. It’s possible to scan patient histories and compare them to massive data pools. In doing so, physicians can identify potential issues for both preventative healthcare and treatment. Additionally, AI has been shown to reduce post-acute care discharge waits, so patients’ time will be given more value as well.
Saving time and money are impressive examples of how machine learning in healthcare can benefit everyone. Of course, the medical field comes down to saving lives. Fortunately, machine learning has excelled in this endeavor. An empirical review of 20,000 studies found that such programs perform better than trained physicians.
The study found that AI with machine learning capabilities accurately gave an “all clear” diagnosis 93% of the time. For doctors, this number was 91%. What may be even more important, though, is that deep learning systems could accurately detect a disease 87% of the time. Physicians got it right in 86% of cases.
These differences may seem negligible, but those small percentages add up with hundreds of millions of doctor visits yearly. Even if machine learning in healthcare only improves outcomes 1% of the time over physicians — a conservative estimate for the future — those are millions of interactions that end more positively for patients.
When GBT announced Avant AI, it introduced the world to a powerful cognitive computing resource. It works by taking vast stores of data — most of it unstructured — and putting it into context to derive value. This complex process uses statistical models, a wide range of sources, neural network algorithms and more.
The easiest way to understand this process, though, is to break it down into simple steps:
- Avant AI seeks out potentially millions of articles within a specific domain. These can be research papers, blogs, media reports, and more.
- After reviewing all these sources, the platform narrows its findings to about 100 sources that offer the most insight.
- Avant AI further eliminates less useful sources until it’s only left with a few dozen results.
- The program scans about 1,000 sentences within these findings to better understand their meanings and relations.
This process could be helpful in any industry, but machine learning in healthcare can save lives. Avant AI is undergoing improvements to enable robust Q and A capabilities. Not only will this offer medical professionals the ability to use informative chat dialog interfaces, but they’ll also have a robust health advisory system at their disposal.
Avant AI provides a first-line tool for medical professionals and an improved interface for patient-computer interactions by connecting to the most credible health-related sources. In the future, this will allow doctors to offer improved telemedicine, more accurate diagnoses and better assistance for remote locations. The latter benefit is significant since millions of Americans live more than half an hour from the nearest hospital.
The best part of Avant AI and other tools for machine learning in healthcare is that they’ll continue to improve. That’s the entire point of machine learning. Artificial intelligence is changing the medical field for the better. While platforms like Avant AI may currently serve as health-advisory systems, there’s little doubt that continuing improvements in these tools will continue to change the way we offer healthcare.
Artificial intelligence has found its place in nearly every part of our lives. From choosing our next Netflix binge to assisting in open-heart surgery, the possibilities seem endless. Of course, one of the examples mentioned above is far more consequential than the other. This is why medical professionals must quickly get on board with utilizing the best tools for machine learning in healthcare.
Avant AI is one of those tools. It has proven to be an exceptional example of Artificial General Intelligence. When the world experienced one of the most significant health crises ever, though, the true potential of Avant AI became clear. There are many benefits to utilizing this knowledge-based health advisory system, but the most important is undoubtedly the ability to offer improved outcomes for patients.
Regardless of the underlying tool, however, integrating machine learning in healthcare will remain a necessity.