What do you understand when you hear the term ‘Machine Learning’ (ML)? Machine Learning is a subset of Artificial Intelligence (AI) that provides systems the ability to automatically learn, as well as improve, from old data and past experiences without being specifically programmed. ML mainly focuses on the development of various computer programs such that they can easily access business data and make use of it in order to learn on their own using Deep Learning techniques.
ML has made its way through numerous business sectors including retail, financial services, telecommunication, real estate, etc. However, it is worthwhile to have a look at the immense growth and improvement it has made in the healthcare industry.
According to one of the leading consulting firms today, McKinsey, ML in healthcare may generate over US$100 billion on an annual basis in terms of decision-making, improvements in the efficiency of clinical experiments and research, and the creation of new tools for physicians, regulators, insurers, and consumers.
If you wish to make improvements and technological advancements in the medical industry, then a career in ML is the best and most suitable path for you. So, sign up for the Machine Learning online course and be a part of creating change in this vast industry.
First, learn about the 5 most popular applications of the Machine Learning technology in the field of healthcare.
ML Making Improvement in Illnesses Diagnosis
With the population growing constantly and the increase in life expectancy, healthcare systems have become under-resourced and overburdened. They do not have enough equipment to face the number of challenges emerging these days.
To overcome these challenges, researchers and scientists have developed specific ML models that can interpret the illness during its early stage. These models help in predicting diseases such as cancer, genetic diseases, etc. that are otherwise difficult to diagnose.
The heart is a significant organ of the human body. However, it is extremely difficult to diagnose heart diseases such as Coronary Heart Disease (CHD), Coronary Artery Disease (CAD), and others. With the help of ML, scientists are developing an automated diagnosis system for heart diseases. It uses algorithms such as Naïve Bayes and Support Vector Machine (SVM) to develop machines that can help in the detection of heart diseases.
Better Medical Imaging Diagnoses Due to Machine Learning
As per researchers from IBM, medical images are one of the biggest data sources in healthcare. ML algorithms have the ability to process large amounts of medical images at high speed. These systems can also be trained in discovering precise details in MRIs and CT scans.
A few technologically advanced companies have also built ML-based analysis of various types of medical imaging reports that have the capability of diagnosing abnormalities or tumors with high accuracy as compared to the manual analyses done by the healthcare professionals.
With ML, Google’s Lymph Node Assistant (LYNA) allows you to detect the early spread of breast cancer. Moreover, a Deep Learning convolutional neural network (CNN) has the ability to diagnose cancer in a more accurate manner when compared to dermatologists who do it following the traditional methodologies.
ML Progressing Toward Drug Manufacturing and Discovery
You can apply ML concepts in any of the stages of discovering a new drug, including in the phases of designing the chemical structure, investing drug safety, target validation, and managing clinical trials. With ML being used in the field of drug discovery, the main aim is to reduce the cost of bringing new drugs to the market in a significant manner. Besides, this will also make the process of drug discovery faster and cost-effective.
Companies use Deep Learning software to filter tons of molecules in a couple of days, which normally takes months using traditional methods. Further, they analyze simulations that provide an idea of how a specific medicine will react in the human body. These software have also helped in discovering potential drugs for multiple sclerosis and Ebola.
Machine Learning Helping Develop Personalized Medicine
By applying ML to numerous data sources such as electronic health records, genetic data, and environmental and lifestyle data, scientists are moving in the direction of developing customized treatments for cancer, depression, and many such diseases. IBM Watson Oncology is achieving heights in developing a treatment for cancer by leveraging the medical history of individual patients and generating various treatment options for them.
Customized treatments can be more effective if you make use of individual health and predictive analytics with the available data for further research and better assessment. At present, scientists are limited to choose from particular diagnostic sets or estimate patient risks based on their genetic information and symptomatic history. However, with ML, you can eliminate most risks and offer a better and much faster diagnosis.
Possibility of Robotic Surgery with Machine Learning
Before Machine Learning, robotic surgery was just a concept that was yet to become a reality. Ever since its materialization, this ML application is used in—suturing, improving robotic surgical materials, surgical skill evaluation, and surgical workflow modeling.
Suturing is a process in which surgeons sew up open wounds. Automation of this phase of surgery might help in reducing the length of the surgical procedure, along with surgeon fatigue. With robotic surgery, doctors have the capability to operate successfully with precision in complicated scenarios.
The da Vinci robot (aka da Vinci surgical system) allows surgeons to manipulate and control the limbs of the robot in order to perform surgeries with accuracy and lesser tremors in the tight parts of the human body. In addition, doctors also use robotic surgery for hair transplantation since the process involves precision and fine detailing. Robotics powered by technologies such as Machine Learning and Artificial Intelligence has enhanced the accuracy of surgical tools.
As per Accenture, the surgical system has reduced the time frame of surgeries by approximately 21 percent in the past year.