Machine learning is changing the way we live, impacting everything from the tech industry to agriculture, insurance, banking, and even marketing. However, the domain where machine learning is having the most important impact is healthcare.
Machine learning applications in healthcare combine the processing power of millions of human minds to accelerate and revamp such fields as diagnostics and medicine, changing the way we live and working to increase longevity.
The Role of Machine Learning in Healthcare:
Although computers and robots will probably likely never completely replace nurses, doctors, and other essential healthcare professionals. ML is now being used in healthcare to give superior patient care and has provided real-world results.
- According to a recent Forbes article, machine learning will become over a $6.6 billion investment for healthcare in 2021.
- ML is revolutionizing the healthcare industry and changing the way doctors think about providing care by reducing time wastage and other mistakes attributed to human error.
- Today, machine learning supports personalized care through improved diagnostics, predictive analysis, and prescriptive healthcare recommendations.
What Makes Machine Learning Essential in Healthcare?
Machine learning algorithms swiftly process huge datasets and give useful insights that allow superior healthcare services. Although the healthcare industry was hesitant to adopt machine learning, it is now quickly catching up to provide successful protective and prescriptive healthcare solutions.
Hospitals by and large are quickly making room machine learning programs because of their ability to swiftly analyze voluminous datasets and classify patterns in order to provide more accurate diagnoses and better patient care.
Instances of Using Machine Learning in Healthcare:
As the development of machine learning solutions has become more mainstream and cost-effective, real-world integrations have helped hospitals and the healthcare industry differentiate themselves. For example:
- In an article published by CB Insights, machine learning is being leveraged by 22 healthcare companies to make new programs for diagnostics and imaging. This field is apt for the application of machine learning because computers and deep learning algorithms are good at understanding and recognizing patterns, which is of prime importance in diagnostics.
- Pathway Genomics is working on creating a one-of-a-kind blood test, which can determine whether early detection or prediction for that matte of certain types of cancer is possible or not using the application of machine learning.
- Similarly, a health data science company, Lumiata, is working on developing predictive analytics tools for discovering insights and making predictions for symptoms, procedures, diagnosis, and medications for patients.
Ways Machine Learning is changing the Healthcare Industry:
Machine learning is advancing healthcare for generations to come in five major ways.
- Drug Discovery
Machine learning established new methods in drug discovery, such as precision medicine and next-generation sequencing, which can ensure a drug has the intended effect on patients. With the implementation of machine learning techniques, medical experts can develop algorithms to treat disease progression and design specific treatments for each patient, like those with Type 2 diabetes.
- Identifying Disease & Their Symptoms
Machine learning aids medical experts in determining the risk for each patient, depending on their symptoms, past medical records, and family history. ML streamlines the process of finding treatments for evolving illnesses, as well as helping researchers to track possible pandemics and to understand better why some diseases are more prevalent in specific cultures and demographics.
- Help to Create New Diagnostic Tools
Modern hospitals are high-tech environments run by advanced machines and the staff who are trained. The hospitals are increasingly shifting towards automation, to a future where diagnoses can be made accurately. Machine learning can accelerate disease diagnostics and make the risk of misdiagnosis less likely.
- Virtual Assistants
Using the real-time data analysis process of machine learning, a future healthcare trend could be giving patients the independence of tracking their own vitals with the guidance of a wearable device integrated w/ machine learning software. This could be instrumental in tracking the next step for patients, dietary guidance based on a condition, or to track and respond to major vital spikes.
- Clinic Performance
Something that is too often overlooked by the general public is the need for hospitals and clinics to align themselves with evolving compliance protocols. With the implementation of ML techniques, it becomes easier for hospitals and clinics to keep track of new or changing policies. Machine learning takes the guess work out of the protocols to treat patients.
How Does Machine Learning Helps to Fight COVID-19?
Researchers around the world work to found out how ML can help in fighting the epidemic. In order to streamline processes, the integrations worked wonders in finding a vaccine.
Without the advancements made by machine learning processes, vaccines for COVID-19 may have taken years to develop. Using machine learning, scientists were able to identify and isolate the SARS-CoV-2 genome and test treatments. Machine learning allowed for scientists to accelerate research, forecast the virus’s evolution, and create COVID-19 vaccinations.
Where to find Machine Learning Experts?
Although the popularity of machine learning is growing inside of healthcare, the supply of specialists is fairly low. Dash Technologies has become a leader in machine learning tools and techniques by gathering machine learning specialists from various industries to provide support to our clients
If you have questions or want to know how ML can be beneficial to you, please reach out.
We’re technology experts with a passion for bringing concepts to life. By leveraging a unique, consultative process and an agile development approach, we translate business challenges into technology solutions.
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