10 Uses of Machine Learning in Healthcare

Healthcare is a vital domain that the world can’t do without. With an ever-increasing demand amidst a global pandemic to boost healthcare facilities and raise the bar of excellence in the field, healthcare professionals are digging into the role of technology that can possibly contribute to this cause in an efficient way. 

 

When it comes to healthcare technology, a lot can go into exploring its whereabouts and finding the true applications of technology in healthcare. Ever since computers became a part of our world, the world of healthcare expanded in terms of data storage and efficient record-keeping. 

 

That said, the role of technology is not only restricted to record-keeping but it is certainly evolving at a rapid pace to unfold the rising use of advanced technologies like machine learning, artificial intelligence, and neural networks. 

 

While it all sums down to efficiency and rapid data evolution, technology can traverse through various horizons in order to amalgamate nascent prospects of ML and AI. This, in turn, can unveil the world of healthcare and medicine to the masses with more transparency and educated decision-making power. 

 

Discussing the role of machine learning, in particular, one needs to take into consideration a lot of factors before processing the true scope of the technology in the domain. The following segment throws light on the top 10 machine learning applications in healthcare to help you understand its role in a better way. 

 

These applications range from predictive analytics in a post-pandemic world to automated procedures that narrow down the manual labor and resources supposed to be invested. 

 

 

Uses of Machine Learning in Healthcare 

 

Suppose you have a magic wand that can do anything for you. What would you wish for? While some would say a day full of treats and delicacies, others would certainly look at the bigger picture and aim to improve the world of healthcare. 

 

The only difference is that magic lies in our hands now that we have the power to combine machine learning models with health and medicine. Let us discuss in brief the uses of machine learning in healthcare. 

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Uses of Machine Learning in Healthcare

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1. Disease Classification

 

One of the topmost applications of ML in healthcare is that it can help in disease classification. Using machine learning algorithms and cluster sampling, diseases can be categorized on the basis of their symptoms and clubbed into different categories using machine learning. 

 

This is a fairly simple task and requires customized algorithms empowered by AI and ML to help doctors and researchers place diseases in various categories. 

 

This is also related to the application of disease diagnosis using the classification algorithms to help identify tough diseases or the ones that might be hard to catch. 

 

“Machine learning algorithms can help to determine and label the kind of disease or medical case you’re dealing with.”

 

- Disease Classification using ML 

 

2. Predictive Measures

 

The second use of ML in healthcare is that it helps in predicting phenomena, health risks, plausible pandemics or outbreaks by extracting useful insights from the past data available. 

 

Using predictive analytics, such forecasts can be made beforehand. With the help of machine learning tools and software applications that are capable of determining future possible outcomes or phenomena, this is a very helpful use of ML in this field. 

 

In addition, such a purpose can also indicate the plausible symptoms of a disease that is already on the rise. 

 

For instance, when the Covid-19 pandemic broke out, predictive analytics was not very useful as the world had seen such an outbreak of one of a kind. However, as time went by, data reports and patient records were analyzed in order to extract future symptoms or future effects of the pandemic on those affected. 

 

This was a very useful application of technology that helped minimize and contain the disease manifold. 

 

3. Automated Procedures

 

Another powerful application of ML in healthcare lies in the hands of operative procedures that were traditionally performed by doctors and a team of nurses, anesthesiologists, and other skilled professionals. 

 

Automated procedures are easy operative procedures that can be performed by machines powered by ML and AI algorithms. With the help of customized technology and trained equipment, automated procedures require less manual labor and resources and result in equally good results. 

 

This way, hospitals need to deploy less staff for such procedures and invest more in such machines that have a long shelf-life supported by regular maintenance. 

 

4. Detecting Anomalies

 

While machines can skillfully perform easy operations and less technical procedures, there is more to the machines and their ability than just doing what they are trained to do. 

 

This brings in the use of machine learning in healthcare to detect abnormalities or irregular outcomes. From operations to research protocols, machines induced with the technology of ML can react to anomalies as soon as they are made available to do so. 

 

This is a boon for the healthcare industry as not only does it detect anomalies and irregular patterns but it simultaneously keeps a record of everything. This is a useful application as many of the procedures performed in the industry across various horizons can go unnoticed or even irregular sometimes.

 

5. Drug Development

 

The very first thing that comes to our mind when we hear about medicine is drugs. Medicines are as important as the entire industry is and it goes without saying that drug development also requires technology at its forefront. 

 

From research and development (R&D) to trials, machine learning can change the face of drug development and equally alter the course of its evolution. By employing big data analytics and machine learning tools, drug development can go a long way with respect to drug development. 

 

“This also includes R&D technologies such as next-generation sequencing and precision medicine which can help in finding alternative paths for therapy of multifactorial diseases. “

- Drug development in Medicine 

 

6. Bot System

 

What’s more, the use of ML in medicine can also indicate the development of healthcare applications and the bot system. Healthcare applications, among other software apps on mobile and desktop, have been a magic wand for the masses during the times of the pandemic. 

 

Using the bot system, healthcare facilities like hospitals and medical centers can deploy chatbots at the forefront to communicate with patients and care-seekers and process them further for one-to-one conversation. 

 

The bot system is a comparatively easier and more efficient use of ML as it helps to reduce staff requirements at the consultation levels. 

 

7. Image Identification

 

The world of medicine receives a lot of unidentified patients and care seekers who have no way of going back to their families. The use of image identification using ML helps in identifying lost patients and tracking their unknown relatives and families. 

 

In the tough times of calamities and outbreaks, image identification also helps in an efficient way of keeping records of patients by registering them with their names and images for better recognition later on. 

 

This application is also used when it comes to clinical research and trials in order to segregate candidates as per their health charts and records. Even though it may be minimally impactful, this particular use goes a long way in the field of healthcare. 

 

8. Streamlined Treatments

 

While the field of healthcare is already an organized setting of patient records and operations performed, there is a need and a dire demand for more streamlined processes from top to bottom. 

 

This means that by using ML, medicine can be made more streamlined and organized by using customized portals and interfaces for doctors and nurses to maintain patient records and visits on a regular basis. 

 

From the very first consultation to the discharge papers, patient records can be streamlined in a way that helps the hospital to keep a track of activity in each and every case. 

 

 

9. Rapid Research Procedures

 

One of the biggest advantages of machine learning in healthcare revolves around rapid research procedures. In order to find cures for diseases, research has to be performed across various horizontals. 

 

Moreover, research procedures also involve past data analysis with possible outcomes in the future. All these requirements can be simply solved with the help of one technology - machine learning. 

 

Research procedures can be made easier to conduct with the help of machine learning tools in order to strategically proceed from one end to the other. As machine learning becomes more and more advanced, such procedures will be easier to perform with the help of advanced technology and related equipment. 

 

10. Record Keeping 

 

Lastly, the use of ML and the entire technology field in the healthcare industry is centric to record keeping. By using categorization and ML classification, record keeping has become far more organized and computerized at the same time. 

 

From the very first time the patient enters into the hospital till the time s/he steps out of it, hospital staff can maintain all types of records - medical, administrative, and financial, to keep up with increasing patients day by day. 

 

This goes with the task of presenting information and performance evaluation at the time of audits to make sure that all records are in place. 

 

Future of Healthcare 

 

The future of healthcare, when combined with that of rapidly evolving technology seems bright. As long as the technology sector is benefitting the field of medicine in and out, the future holds a lot many unexplored surprises for the world. Who knew that one-day robots could enter the operating rooms and operate on patients? 

 

Well, with the changing face of technology, the field of medicine has also kept up with the course. To sum up, machine learning is a major contributor to the growth of the healthcare industry. 

 

Had it been distant from this world, hospitals would still have maintained paper records, research centers would still have invested years in conducting clinical trials, and the world of medicine would have been no faster than it used to be. 

 

However, thanks to machine learning and other technologies like AI, understanding medicine has become rapid, responsive, and ravishing!