How is machine learning becoming a game-changer in the healthcare industry?

Sudeep Srivastava March 9, 2022
Top applications of machine learning in healthcare

Machine learning is driving massive improvement and innovation in the healthcare industry. It is expediting advances in clinical operations, drug development, surgery, and data management. 

The Covid-19 pandemic has further pushed the healthcare sector to actively adopt this modern technology.

More importantly, patients are set to benefit the most as the technology can improve their health outcomes by analyzing the best treatment plans for them. ML is capable of detecting disease at an early stage more accurately, helping to reduce the number of readmissions in hospitals and clinics.

In this article, we will find out the key applications of machine learning in healthcare and how this technology is redefining the industry with its exceptional benefits.

Let’s begin!

Major applications of machine learning in healthcare

From maximizing hospital efficiency to making an accurate diagnosis, ML technologies have proven to be a boon for the healthcare industry. Here are some major machine learning applications in the healthcare industry to better engage with users and generate more revenue. 

applications of machine learning in healthcare

Personalizing treatment

Offering personalized treatments is one of the key machine learning use cases in the healthcare domain. It allows healthcare organizations to deliver personalized patient care by analyzing patients’ medical history, symptoms, and tests. Using ML in medicine and healthcare, doctors can develop customized treatments and prescribe medicines that target specific diseases in individual patients.

With ML, healthcare organizations can also have access to the analysis based on electronic health records for the patient. This helps doctors make faster decisions on what kind of treatment best suits the patient. 

Additionally, machine learning in healthcare can assist doctors in finding out if the patient is ready for necessary changes in medication. This helps induce the right treatment from the beginning.

Detecting fraud

According to the Justice Department of the US, 3% of healthcare claims in the country are fraudulent. This converts into a hundred billion dollars lost annually. Using machine learning models, the healthcare industry can detect invalid claims before they are paid for and speed up approval, processing, and payment of valid ones. Apart from detecting insurance frauds, ML also prevents the stealing of patient data.

Leading healthcare organizations such as Harvard Pilgrim Health are embracing AI and ML technologies to root out healthcare fraud. They are using ML-based fraud detection systems to identify claims and detect suspicious behavior.

Detecting diseases in early stages 

There are a lot of diseases that you need to detect in the early stages to identify the treatment plan and help patients secure a good way of living. 

A combination of supervised and unsupervised algorithms under machine learning provides better assistance to doctors in the early detection of diseases. ML compares new data with the old data on a particular disease, and if the symptoms show a red flag, the doctors can take action accordingly.

Robot-assisted surgery

ML-powered surgical robots have revolutionized surgeries in terms of accuracy and speed. These systems can perform complicated surgical procedures with reduced blood loss, side effects, or pain risks. Additionally, post-surgery recovery is much faster and easier.

Maastricht University Medical Center is one of the best machine learning in healthcare examples. It has been using an ML-powered surgical robot to suture small blood vessels, no thicker than 0.03 millimeters. 

Using ML in medicine and healthcare, professionals and surgeons get access to real-time information and insights into a patient’s current health condition. This, in turn, enables healthcare providers to make intelligent decisions before, during, and after procedures to ensure the best outcomes. Read here to know how the future of work will be molded by robots

Analyzing errors in prescriptions

In the US alone, 5,000 to 7,000 people die annually because of prescription errors. These errors often stem from flawed EHR interfaces — doctors choose the wrong drugs from a drop-down menu or get confused in dosing units. In such cases, ML technologies can be a savior. 

ML models analyze historic EHR data and compare new prescriptions against it. Those prescriptions that deviate from typical patterns get flagged, so doctors can review and adjust them.

For instance, Brigham and Women’s Hospital uses an ML-powered system to pinpoint prescription errors. Over a year, the system identified 10,668 potential errors, and 79% of them were clinically valuable, so the hospital managed to save $1.3 million in healthcare-related costs.

Along with cost savings, an ML-powered error detection system increases the quality of care by preventing drug overdosing and health risks. 

[Also Read: EHR Optimization Guide to Make Your Healthcare Offering Efficient]

Assisting in clinical research and trials

Clinical research and trials are costly and lengthy processes. There is a good reason behind this – new drugs and medical procedures should be proven safe before being used widely. However, there are cases when the solution needs to be released as soon as possible – like with the vaccines for COVID-19.

Fortunately, machine learning algorithms can make the process shorter. These algorithms can help determine the best sample for the trial, gather more data points, analyze the ongoing data from the trial participants, and reduce the data-based errors.

Drug discovery and creation

It is one of the main benefits of machine learning in healthcare. ML has the ability to discover new drugs that offer great economic value for pharmaceuticals, hospitals, and new treatment avenues for patients. It also makes the process of drug creation faster and extremely cost-effective.

Atomwise is one such pharmaceutical company that makes use of supercomputers, which roots out therapy from the databases of molecular structure. In 2015, Atomwise used its deep learning technology to find out the current medicines in the market that could be redesigned for treating the Ebola virus. They successfully found two drugs that could help reduce the risks of the epidemic. 

The analysis that would have taken several years happened in one day through Atomwise ML-based technology.

[Also Read: How digital transformation is reshaping the healthcare industry?]

Automating image diagnosis

Hospitals and clinics use ML to recognize abnormalities in different kinds of medical images, such as MRI or radiology scans. Image recognition assists doctors in diagnosing liver and kidney infections, tumors, improving cancer prognosis, and more.

The best example of ML-powered visual perception is the tool utilized by the UVA University Hospital. Utilizing ML algorithms, the tool analyzes biopsy images of children to differentiate between celiac disease and environmental enteropathy, doing it as reliably as doctors do.

Read case study

Now that we have looked into the key machine learning applications and machine learning use cases in the healthcare industry, let’s dive into the challenges of implementing ML technologies relevant to the healthcare industry.

Challenges of adopting ML in healthcare

The wide implementation of innovative technologies like AI and ML comes with several challenges. From the lack of quality data to patient safety, a number of roadblocks exist for the healthcare industry utilizing ML-based software and technologies.

So, let’s take a look at them: 

Challenges of adopting ML in healthcare

Patient safety

The decisions made by machine learning algorithms completely rely on the data it has been learned on. If the input is unreliable or wrong, the result will be wrong as well. The flawed decision can harm the patient or even cause their death.

Lack of quality data

The results you get from machine learning algorithms depend on the quality of data put into them. Unfortunately, medical data is not always as precise and standardized as it often needs to be. There are gaps in records, inaccuracies in profiles, and other difficulties. So, before you apply a machine learning tool, you’d need to spend time gathering, cleaning, validating, and structuring data for its purposes.

Privacy concerns

Another key challenge of implementing AI and machine learning in healthcare lies in  the amount of data collected that contains sensitive or confidential information. This, in turn, requires additional security measures to be implemented. So, it’s crucial to look for the right ML software development company that can offer a number of security options to ensure your customer data is appropriately handled. 

The future of ML in healthcare

The future of ML in the healthcare sector looks bright. Despite some challenges, ML is already enhancing the patient experience, medicine practice of clinicians, and the pharmaceutical industry operations. And the journey has just begun. According to Grand View Research, the global AI and ML in the healthcare market are expected to expand at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030. 

The growing datasets of patient health-related digital information, increasing demand for personalized medicine, and the rising demand for reducing care expenses are some of the major driving forces of the market growth. 

Additionally, in the coming years, there could be programmed robots that would assist doctors in the surgery room. ML-powered technologies in healthcare can enable doctors to minimize the risk during operations by going down to the minutest details of the treatment.

Machine learning in the healthcare industry is also enabling “virtual biopsies” and advancing the innovative field of radiomics. Leveraging machine learning and AI tools to gain insights can create faster, more accurate alerts for healthcare providers. 

AI and machine learning in healthcare can also provide earlier warnings for conditions like seizures or sepsis, which often require intensive analysis of highly complex datasets.

Leveraging ML for risk scoring, clinical decision support, and early alerting are some of the significant areas of development for this revolutionary approach. 

Machine learning is surely going to expand its base in healthcare in years to come. Hence, healthcare professionals and clinicians must start utilizing machine learning in their favor.

Talk to us

How can Appinventiv help your business adopt machine learning?

At Appinventiv, our team of professionals can help develop custom machine learning-based software solutions considering your healthcare business goals. Our technical knowledge and experience in the industry can help you bring your vision to life. 

One of our successful projects in this field is the YouCOMM app developed to connect in-hospital patients with nurses for medical help in real-time. The system allows patients to call/notify the staff through the use of head gestures or voice commands. 

Since the app launch, 5+ hospital chains in the US have been running on the YouCOMM solution.

If you are also looking for AI and ML software development services or want to understand how machine learning is used in healthcare, get in touch with our experts. We can help you implement machine learning in healthcare solutions and cater to your needs in the most tech-friendly manner.

THE AUTHOR
Sudeep Srivastava
DIRECTOR & CO-FOUNDER
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