Machine Learning in Healthcare

10 Innovative Applications of Machine Learning in Healthcare

“A breakthrough in Machine Learning would be worth 10 Microsofts”. – Bill Gates

COVID-19 has shown us the reality of our healthcare system.

Despite the technological innovations in the healthcare industry, still, it is failing to keep patient outcomes on the top priority.

But with the help of machine learning in healthcare, hospitals, clinics, and other healthcare entities can improve their patients’ clinical outcomes while at the same time reducing the overhead and medical costs.

So, let’s take a deep dive into the ocean of machine learning in the healthcare industry.

10 Game-changing applications of machine learning in the healthcare

Machine learning is taking place in the healthcare ecosystem in several forms.

For instance, predicting clinical outcomes using large-scale medical data of the patients and identifying medical errors are a glimpse of machine learning in healthcare.

1. Identification of the disease and clinical assessment:

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To become a successful healthcare organization, medical professionals must have the ability to recognize the health condition of the patients with speed and accuracy.

In other words, their clinical assessments need to be immaculate and patient-centric.

Machine learning integration with healthcare technologies can identify the patients’ disease while at the same time creating accurate and result-driven care solutions for them.

For instance, integrating machine learning algorithms into wearable devices can detect Parkinson’s disease even its progress.

2. Efficient health record management:

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Governing up-to-date medical or health records of the patients’ medical data is not a straightforward process.

In addition to this, manual data entry and sharing is a time-consuming process for the healthcare professional, especially at the point of care.

It sometimes results in manual errors which can put the patients’ medical condition in a life-threatening position.

To avoid that, machine learning in healthcare plays an important role.

It makes health record management faster and more efficient which initially saves the time and money of the healthcare ecosystem.

3. Drug development or discovery:

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One of the significant roles of machine learning in healthcare is clinical support in drug discovery.

It offers a great economic value to healthcare facilities and pharmaceutical companies by identifying the pattern in the drug development data.

Tech giants such as IBM and Google have developed a machine learning platform to find the ideal clinical assessment procedure for the patients.

4. In medical imaging:

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Machine learning and deep learning algorithms both are big players in making medical imaging more efficient.

With the large scale of the data, deep learning algorithms are accelerating the diagnosis process.

However, both require more data to provide accurate and faster results every time.

Nevertheless, the combination of healthcare professionals and ML-driven tools can boost the medical image diagnosis procedure along with deciding whether the treatment approach is correct or not.

5. Behavioural identification:

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This is a big step in clinical assessment and drug development.

Identification of patients’ behaviour based on their medication and care approach can help the healthcare professionals to identify what is lacking in the clinical assessment.

As a result, many health tech startups are focusing on the behavioural health of the patient to prevent and detect serious illnesses such as cancer and other chronic conditions.

6. Robotic surgery or assistance tools:

Doing surgery or supporting patients with robotic tools is becoming a new norm in the healthcare industry.

And machine learning is helping significantly in many forms.

For instance, an ML-based assistive therapy device gathers data on patients’ body motion while they are doing an exercise for recovery.

Later, that data helps the PTs to analyze the motion so that they can provide feedback to the patients.

7. Machine learning in healthcare as a personalized clinical assessment:

Just imagine the outcome when patients are getting treatment solutions such as medication based on their medical condition.

But right now, healthcare professionals don’t have enough tech solutions to offer a personalized clinical assessment and predict the outcomes from it for every disease.

For instance, IBM Watson has created a tech solution for oncology that generates multiple clinical assessments based on patients’ medical records.

In the upcoming years, we will see more ML-based health tech solutions that allow healthcare professionals to provide personalized care solutions for every disease with accuracy.

8. Clinical trials:

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Machine learning can become an economical solution for the pharmaceutical industry and clinical trial rooms.

Because clinical trials require a large investment capital and sometimes the discovery phase takes years of time.

Machine learning uses predictive analytics to identify suitable participants for clinical research so that the trial centers can achieve their research goals in the desired time.

9. Improvement in medical device performance:

Offering care solutions with the help of medical devices and tech solutions are now normalized in the healthcare ecosystem.

For instance, ventilators, glucose monitoring devices, heart-lung machines and much more devices give a life-important measurement at the point of care.

Thus, it is essential to improve the performance of such devices along with maintaining their conditions.

With the help of machine learning in healthcare, organizations or facilities can increase the performance of devices and can predict the maintenance requirements.

10. Machine learning in mental health solutions:

ML is a big player in decoding the mental health concerns of the patients.

Undeniably, it also can fill the healthcare gap for many health conditions.

But, poor mental health is one of the leading concerns across the globe right now.

ML helps to predict the outcome of the mental illness of the patients by analyzing the large-scale data regarding anxiety, depression, stress and many more.

In fact, implementing ML algorithms in IoT devices like sleep trackers and other health tech solutions can help healthcare professionals to improve care outcomes.

Power the healthcare ecosystem with machine learning algorithms

Predicting healthcare outcomes is not magic, it’s Artificial Intelligence and Machine Learning.

Both can benefit to create a path toward leveraging affordable, faster, accurate, and suitable healthcare solutions for any health-related concerns.

However, the implementation of AI and ML into digital health tech solutions needs to be performance-driven.


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