How Machine Learning is Revolutionizing Healthcare

How Machine Learning is Revolutionizing Healthcare

Machine learning is one of the technological changes that is advancing the medical field today, with many advantages being held for the industry.

Artificial intelligence and big data go hand in hand, and nowhere is this a better fit than for the healthcare industry. 

Healthcare is a field that mainly before 2020 was stuck in paper-heavy processes with little technological innovation on the operations side. But since the COVID pandemic and the boom of telehealth services, this has quickly come to change.

Machine learning is one of the technological changes that is advancing the medical field today, with many advantages being held for the industry - all of which we’ll describe in more detail below: 

How Is Machine Learning Changing Medicine?

The first way machine learning is transforming medicine is by improving how data is handled. Large amounts of data are now processed via machine learning methods in a more affordable way. The technology does this by improving the speed of analysis, providing tools for discovery or prediction, and forecasting potential risks.

The next way that machine learning is playing a powerful role in the healthcare industry is in diagnostics. Disease identification and diagnosis of patients can both be improved by machine learning algorithms that have access to global big data sets. This can be used to identify disease indicators, personalize the experiences of patients and predict future epidemics.

One such example is machine learning algorithms that are used in the interpretation of medical imagery such as X-rays  - a process that has proven as accurate as a human interpretation by radiologists. 

Another aspect of machine learning in healthcare that is proving promising is that of precision medicine. Vast amounts of patient information can be processed by a machine-learning algorithm in order to identify patterns or treatments that would be most effective for an individual patient. This makes for a better outcome for healthcare staff and patients alike.

Then as mentioned above, there is the operations side of the healthcare industry, something which is also being transformed by AI. Administrative tasks amount to a large amount of the work in healthcare operations, and machine learning algorithms can assist with lesser tasks like appointment scheduling, managing health records, and assisting with medical billing. This frees up valuable time for medical staff to provide better care to their patients.

Lastly, there is the issue of cost. Cost reduction is one of the most significant advantages of machine learning in healthcare. By automating tasks like the administrative ones already described, healthcare institutions can now save on labor costs while becoming more efficient at using the resources they do have.

Why Is Machine Learning Important In Healthcare?

Machine learning is important to healthcare advancements in multiple ways, but they all point back to one goal: improving healthcare experiences for both the medical provider and the patient alike.

This technology is just one of many that are helping the healthcare industry to move forward - and this is another key thing. Medicine is a field of constant evolution, and integrating technologies like AI will ensure that the industry is primed for future advancements to come.

But perhaps the most interesting application of machine learning in healthcare is making predictions regarding new epidemics or pandemics. The COVID-19 pandemic opened our eyes to how vulnerable humanity is to the outbreak of new diseases and how unprepared we were. With machine learning algorithms, however, we could track satellite data, news reports, and even social media sources to determine how an illness is spreading and when it will become viral. For physicians and other healthcare staff, this means more time spent on patients and less on searching for information.

So ultimately, machine learning will have a lasting impact on both healthcare providers and treatment centers when it comes to illness detection, custom treatment approaches, clinical research, and much more. 

What Is The Role Of Machine Learning In Health Care Going Forward?

We’ve discussed a few of the applications of machine learning that are already taking place, but it’s also important to look to the future for new applications as well.

In telemedicine, for example, machine learning agencies are already developing solutions that will organize patient information during a telehealth session, automatically capturing information during virtual meetings in order to streamline data handling.

And the uses expand into other aspects of medicine too, such as the pharmaceutical sector. Here, machine learning is being used in drug development by showing drugmakers how patients will respond to various drugs by studying patterns of previous data - allowing medicine to become more accurate and personalized. 

Lastly, the FDA has also passed some policies that allow medical devices to make use of machine-learning technologies - this could change things like how we track chronic conditions and much more.

Real Healthcare Machine Learning Use-Cases

There are several examples of already existing machine learning use cases in the healthcare sector being built by some of the largest tech innovators in the world.

Let’s start with Microsoft, which has a machine-learning division called Project InnerEye. This part of Microsoft makes use of computer vision paired with artificial intelligence in order to identify different types of tumors and other anatomy using 3D radiology images - all of which help surgeons to be more accurate during procedures.

Then when it comes to handling big data, companies like Tempus are processing large amounts of clinical data related to cancer in order to assist with genomic profiling, matching for clinical trials, and other academic research purposes. KenSci serves a similar purpose in the study of big data, but it uses the results in order to predict illnesses and their best course of treatment. It does this by identifying risk markers and modeling potential disease progression.

The Key To Machine Learning Solutions For Healthcare Providers

Machine learning is a field with a lot of potential applications, but also a lot of potential pitfalls when it comes to implementation. Finding an existing solution isn’t always possible for example, or it can be costly to integrate with existing systems already being used.

It makes more sense to create a custom machine-learning solution that is built to meet all the needs of the particular healthcare provider that is in need of evolution. Here we find another problem: hiring for a project like this comes with a lot of hidden costs as you vet, onboard, and train a team to build it.

A better solution is to deal with an existing machine learning team that already has the experience together to bring a custom machine learning solution to life without all of the pitfalls that a new AI builder would step into.

Work With A Leading Machine Learning Agency

NineTwoThree Venture Studio is a leading machine learning agency with more than a decade of experience in bringing custom solutions to life - including for the healthcare sector.

We offer services that can help you:

-Look into the future using past and current data. Get rid of assumptions and discover how your customers are really behaving.

-Teach systems to realize text and images as customers go through a check-out flow. Analyze customer behaviors and trends to make smart decisions.

-Analyze data from images or video to look for actionable alerts, find anomalies, or discover trends.

And that’s just the beginning.

After working on dozens of ML projects (and having a dedicated team), our ML scientists know how to train the models to uncover your problem. Our team is constantly testing "what's possible" and is willing to go the extra 1% in accuracy.

Our data scientists can handle all of your data-related requirements including software development, labeling, and modeling. After the algorithm is tested, we wrap the solution in a cloud infrastructure to deliver a fully functioning ML solution.

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