Machine learning in healthcare is transforming the industry and helps to innovate disease discovery and treatment.
Since communities expect their healthcare facilities to provide the best services – from medical treatments, to surgical procedures, to foreseeing future diseases, etc. – machine learning helps healthcare institutions to live up to the expectations. What are the top 10 applications of machine learning in healthcare? Let’s discover.
1. Discovering and manufacturing drugs
Drug discovery is vital to creating and distributing medicines and medical treatments to patients. With machine learning in healthcare, drug discovery is made easier, from initial screenings to success ratings. Running solely on next-generation sequencing, ML is a godsend for pharma companies, because precision is key to discovering and creating drugs that are beneficial and safe for patients.
As of December 2019, there has been a rise in startups dedicated to making drug discovery and manufacturing seamless. From generating novel candidates in the discovery process to designing drugs once they’re approved, startups want to bring ML to the forefront of discovering and manufacturing drugs.
2. Disease identification and diagnosis
Diseases are always evolving. Machine learning in healthcare helps to prepare for such evolutions.
The identification and diagnosis of diseases, at basic face value, cannot be diagnosed without running into obstacles or doing so with errors. Therefore, the industry has its hopes on ML in healthcare to make identification and diagnosis not only easier but also free of errors.
3. Better medical imaging
With the rise of deep learning comes the breakthrough in offering improved medical imaging. In fact, publications are increasingly offering their takes on this phenomenon, professing the potential of machine learning transforming healthcare through medical imaging. They suggest the idea that image diagnostic tools can be used for analysis, which in turn, will help doctors refer to medical imagery from data sources, and provide the right diagnosis.
Nowadays, there’s a need for medicines to work well with individual patients. Instead of a one-size-fits-all approach, the healthcare industry wants to make medicines more personalized.
In China, researchers have studied personalized medicine therapeutics since 2014. In their ongoing research, they look into pharmaceuticals, genomic medicine, and medical devices, which have the potential to personalize medicine, thanks to ML.
As projected, personal medical data of individual patients will be possible with ML applications and algorithms, meaning that health issues can be detected and assessed easier. And, based on the individual’s medical history, potential risks can be predicted and prevented.
5. Behavioral modification
Since behavioral modification is vital to preventive medicine, ML improves this factor by monitoring and recognizing physical and emotional states in patients. According to a 2019 survey, 66% of U.S. mental health facilities were offering behavioral modification as a form of treatment.
As a result, ML allows patients to understand and learn from their behaviors, and make better decisions when it comes to treatment.
6. Smarter health records
Since there’s a rise in needing personalized medicines and treatments, there’s also a growing need for smarter health records. With ML, healthcare facilities can keep up with health records without having to waste so much time to just keep them up-to-date. Since ML strives to save people time, money, and effort, the market size for it has been increasing over the years in the healthcare industry.
Grand View Research projects that the market size for electronic health records in ambulatory and hospital uses will grow in value going into 2027. With the need for better diagnoses, clinical treatment suggestions, etc., health records need to be smarter too.
7. Smarter clinical trials and research
Before ML had risen to where it is today, clinical trials and research were done through time and money (so much of it). But ML, trials were done in small circles, because the technology and app options were limited at the time. According to a survey done between May and July of 2017, the following percentage of usage from respondents were limited to the following methods:
- 18% of respondents participated in clinical trials via text messaging
- 10% participated via smartphone app
- 8% participated by using a wearable device
- 40% didn’t use any services/technology in clinical trials
As you can see, there wasn’t much faith in ML in healthcare than today. Today, ML will help clinical trials and research in the following ways:
- Look for potential clinical trial candidates
- Monitor candidate through clinical trials
- Access candidate medical history
- Select good testing samples
- Avoid data-based errors, etc.
8. Robots in surgical procedures
With ML comes innovations such as robotic surgery. Robots in surgical procedures have made their way in the healthcare scene. ResearchGate suggests that robotic surgical innovations have taken part in various surgeries, including general surgery, urology, and pediatric surgery.
9. Improved radiotherapy
Radiotherapy is another part of healthcare where ML will thrive. With medical imaging and analysis at the forefront of radiotherapy, there’s a need to ensure that they’re accurate, and that patients are safe when undergoing treatment. That’s why ML not only wants to ensure better radiotherapy, but also be able to detect things like lesions based on data that’s already at hand.
According to a recent survey done by Healthcare IT News, 63% of the people surveyed agree that machine learning has already delivered value in areas in specialty care, including radiology, while only 25% disagree and 13% are still on the fence.
10. Accurate outbreak predictions
Finally, as the COVID-19 has shown the world, there’s now a need to predict the next outbreak (if applicable). Today, ML is being used to do real-time monitoring, and predict outbreaks before they get out of hand.
Ultimately, machine learning will take over many industries, including healthcare. As you can tell by these 10 applications of machine learning in healthcare, the healthcare industry is bound to undergo numerous changes and progress substantially.
About the author
Lauren Groff is a writer and editor at UK Writings and Academic Writing Service. As a blogger, she writes about the latest trends in machine learning, coding, and UX design.