If you are a tech geek being updated about all the advancements in the technology sector, then you may probably be hearing a lot about machine learning all across. For many good reasons, ranging from voice-based personal assistants to autonomous vehicles, machine learning and deep learning are everywhere. Every day, a new product, service, or app is unveiled, which uses machine learning for better and smarter process management.
Machine Learning has already got the definition as the ‘hottest technology of the era, which nurtures the growth of some of the coolest products”.
You likely see many machine learning applications while at work or even while shopping. Some real-time examples are how Google Map is showing you the unknown routes to places, suggestions while you make a purchase online, effective communication with friends and family online, etc. Even when you got navigated to this current page from the Google search page, you have certainly benefitted from machine learning’s applicational use. So, the practicality of machine learning applications is there everywhere. This is how emails do spam filtering, e-commerce stores put forth product recommendations, image recognitions applications work, chatbots offer real-time support, and so on. This article will try to educate the readers about the real-time applications of machine learning and some areas where machine learning can do wonders. For those who have a primary understanding of what machine learning is and how it works, further, we will discuss a few real-time applications of it.
Healthcare machine learning applications
The medical practitioners and doctors may be able to predict the diagnoses and outcomes of treatments to patients having terminal illnesses. The medical system, as a whole, will be able to learn from the input data and help patients to save money by skipping unnecessary tests and diagnostic steps. For example, the radiologist’s role may be replaced with machine learning algorithms. A study done by McKinsey Global Institute suggests that by applying machine learning for informed decision making in healthcare practices, the industry may be to benefit from about $100 billion in value addition to innovation, improved efficiency in the clinical trials, and by introducing many innovative tools for the physicians and insurers.
However, computers and robots cannot replace nurses and doctors fully, but the use of life-saving technologies in machine learning can really help transform healthcare practices with a positive twist. While we discuss the accuracy of machine learning-based applications, the more data we get to feed, the more efficient the results of machine learning algorithms may give you. The future of the healthcare industry is residing on this goldmine of data.
Drug research and manufacturing
Drug research and manufacturing is a very expensive and complicated process that requires a lot of time and needs to fulfills various compound steps. Classic drug discovery may include an unpredictable number of or series of tests to produce a single most effective drug, which humans can use. Machine learning will aid in speeding up this process by cutting short the lengthiness of these multi-stage processes with more accuracy. A proper database structure is very important for medical and drug research studies, and RemoteDBA is offering trustable support in the fields of remote database administration and troubleshooting.
Some real-time examples of machine learning in healthcare
Pfizer users IBM Watson as their technique in immuno-oncology as a technique to see how the immune system of the human body help in fighting cancer naturally. This is one area where IBM Watson uses its gigantic machine learning research to draw out relevant information. It will aid in more effective drug discovery. Pfizer has used machine learning for many years now and sieving through various types of data to empower this research in various drug research and discovery areas. It is being studied in combination with various drugs to determine the best combination for the actual clinical trials.
Personalized medications and treatments
Imagine you have to decide when you need to visit the doctor for some kind of ailments based on the severity. You have MRI and computer systems to help the radiologists detect the existing problems and identify even the smallest possible problems, which the human eye cannot see. After assessing your symptoms, they may give inputs to the computer, which will extract data from the latest research that the doctors need to know and how to make the treatment plan. An AI-based computer system will analyze all your health records to find the interconnectivity in light of your medical history and compare it with the huge store of most recent research data to advise the most appropriate treatment protocol to the person. Nowadays, machine learning is grown enough to have a significant impact on personalized healthcare.
As we can see, personalized medical care has great potential in the future. Machine learning plays a crucial role in finding what kind of genes and makers respond to the particular treatments and medications. Personalized medicines and treatment based on individual health records and analytics is a hot area of research, which provides a better assessment of diseases and ensures appropriate treatments. In the near future itself, we can expect increased use of sensor-enabled and networked devices, smart wearables, and mobile applications capable of monitoring individuals’ health parameters. This will enable a data deluge that can be used effectively to enhance the treatment efficiency. Personalized treatment will ensure optimization of healthcare and also help reduce the cost involved in treatments.
The drug manufacturers may frequently face a big problem: the potential drugs they make probably work on a small sample group on clinical trials, or a small group may turn up unsafe in the trials with significant side effects. A real-time example is Genentech, which is a GNS Healthcare company doing innovations with biomedical data. GNS Reverse Engineering is being used to check for patient response markers based on the genes, which will lead to offering specific therapies in a targeted way to the patients.
More and more healthcare and drug research institutions are adopting machine learning and deep learning practices to strengthen and expedite their research and development. We can see a classic example of such a thing during the time of the global pandemic in the year 2020. Mankind was in need of coming up with a vaccine for Covid-91, whereas the research and trials were largely based on machine learning on big data. In the future, we can expect this technology to grow and widen multifold for the benefit of mankind to improve the quality and expectancy of life.
