With machine learning growing in popularity over the recent years, it has now been considered (and even utilized) in the healthcare industry. Since doctors, scientists, etc. are working around the clock to provide better services, medications, and products to patients everywhere, they now see the potential in looking at the data to better serve them. That’s where machine learning comes in!

Here are 10 things that apply machine learning in healthcare:

Diabetes Predictions

“With diabetes as one of the common and dangerous diseases to date, often leading to other severe illness and death, it’s critical to track it as soon as possible,” says Janna Newton, a business writer at Revieweal and OXEssays. “The good news is, machine learning would be able to detect diabetes at an early stage, thus giving patients a better chance at doing something about it.”

Discover And Manufacture Drugs

Early-stage drugs are in high demand nowadays, since diseases and ailments can happen at any time in someone’s life. The good news is, machine learning allows researchers and scientists in early-stage drug discoveries. Along with R&D technologies (i.e. next-generation sequencing and precision medicine), which can help in finding alternative paths for therapeutic solutions to multifactorial diseases, ML applications are especially beneficial in cancer treatments and personalized drugs. 

Medical Personalization

Personalized treatments matter now more than ever since they do the following:

• They’re more effective by pairing individual health with predictive analytics.
• They can be used for further research and better disease assessment. 

Instead of estimating in assessments with limitations on possible diagnoses, medical personalization – thanks to machine learning – allows doctors to generate multiple treatment options by leveraging patient medical history. With more devices and biosensors to help accommodate this, treatments will soon be available for patients based on their medical history.

Smart Patient Health Records

Machine learning helps you save time, effort, and money by having effective storage systems to put your data in. Therefore, ML is essential for not only keeping health records on file but also keeping them up-to-date. With smart health records, they pave the way for more accurate diagnoses, clinical treatment suggestions, etc.

Smart Clinical Trials And Research

Clinical trials and research are essential to the healthcare industry to see what medicines work, and which don’t work. And with machine learning, clinical trials and research are faster and easier to conduct, since it helps identify potential clinical trial candidates (which can help researchers draw a pool from a wide variety of data points – previous doctor visits, social media, etc.), and provides real-time monitoring and data access of the trial participants. Thus, it helps researchers find the best sample size to be tested, and use electronic records to reduce data-based errors.

Surgical Robotics

As a benchmark ML application in healthcare, robotic surgery is already working to be a promising medical miracle that can help save lives more effectively. And, robotic surgeries are normally divided into 4 subcategories:

• Automatic suturing (sewing up an open wound)
• Surgical skill evaluation
• Improving robotic surgical materials, AND
• Surgical workflow modeling

Surgical robotics not only accurately does a procedure, but it can also reduce human error and or fatigue from a human surgeon. 

Better Detect And Predict Cancer

Today, machine learning is being used to detect and classify tumors at a faster rate, thus promoting more successful cancer detection. It can also use gene expression data from a patient to predict a possible cancer formation in the body. 

Better Crowdsourcing

Crowdsourcing allows medical researchers and practitioners to access a vast amount of patient information (based on consent). From ML-based facial recognition to Medtronic partnering with IBM, crowdsourcing is hard at work to decipher, accumulate, and make available diabetes and insulin data in real-time based on the information, thus improving diagnosis and medication.

Smart Diagnoses

“It’s now more important than ever for doctors to properly diagnose patients,” says Zara Higgins, a project manager at Dissertation Services and Academized, “and machine learning makes all of that possible by helping them point out diseases and ailments that might have otherwise been hard to detect when considering traditional methods. That means hard-to-detect cancers can be spotted and taken care of early without having to guess at anything, or leaving anything to chance. Thus, doctors will be able to provide the right treatments to patients.”

Better Predicts Possible Outbreaks

Today, AI-based technologies and machine learning are used to better monitor and predict possible epidemics around the world. With a large amount of data collected from satellites, real-time social media updates, website information, etc., scientists are able to detect outbreaks before they get catastrophic worldwide. This technology is especially helpful in third-world countries since they lack crucial medical infrastructure and educational systems. 

Conclusion

In a healthcare system, machine learning is very important, since it helps doctors improve their practices, and it can help save lives. With the right knowledge, and bringing information in more convenient ways than ever, machine learning has already been made a part of our lives; and it doesn’t look like it’ll be stopping any time soon, as the demand for better healthcare rises.