Machine Learning in Healthcare

Introduction to Machine Learning

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.

Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Machine Learning in Healthcare

·      ML in healthcare for imaging & diagnosis

With machine learning advancing at an astounding speed, machine learning is an active application in diagnosis of human diseases. As machine learning operates on algorithms, healthcare specialists are aiming to leverage this technology in their field by actively developing algorithms and providing information to machines that can help them in imaging and analyse human bodies for abnormalities. By using smart machines machine on a human body, the machines can quickly scan through the body and can click images to detect diseases early on.

·      ML in healthcare for data collection & follow-ups

Personalization is what humans like when they go anywhere. As big data has several applications and gathers information from every possible source, leveraging the same to improve human life can be helpful for doctors to provide people with enhanced services. When ML can accommodate sufficient information about a user, doctors can personalize the treatment options. This personalization of services is possible with the help of machines providing insights about risks of a particular patient being susceptible to a specific disease. With accurate information and actionable insights, machines can also suggest users and doctors about remedies and precautionary measures with depending on a patient’s response to medications.

·      ML in healthcare for Radiology & Radiotherapy

ML has proved its worth and capabilities to detect cancer in the past and is one of the most viable options for leading healthcare pioneers to identify any abnormalities. With such performance, ML is proving to be another strong option for radiology and radiotherapy. Doctors can use this technology to scan through the possibilities of a patient’s response to a specific input of radiations through their body. ML can also help doctors and surgeons in deciding what and how intense a radiation would be required depending on how well the patient responds to specific amounts of emissions.

·      ML in healthcare for Drug Discovery & Experiments

Scientists strive to find ways of how they can discover newer ways to certain deadly diseases. With rigorous attempts at improving healthcare, they search for different drugs that can behave as advanced medicines and perform experiments that are focused solely on how these medications can help. Machine learning algorithms help scientists by providing them information about how to improve drug performance and behaviour of the same on a test subject. The behavioural details that noted from a test subject and a dummy drug can be noted and ML algorithms can be used to determine how those medications perform on a human being.

·      ML in healthcare for Surgeries

Current technological innovations continuously strive to improve the healthcare situation for patients and doctors. When machines focus on improving the performance of operations, they can help doctors by using surgical robots. These surgical robots prove to be of great help to doctors as they provide doctors with high definition imagery and extended flexibility to reach out in areas that are crucial for a doctor. Machine learning has several other applications in numerous fields that try to improve human life. As healthcare pioneers are working to improve the current scenario of their industry consistently, they can now search for ways in which their organization can leverage this technology and how they can benefit from the same.

Conclusion

Machine Learning has various applications in every field. It plays an important role in healthcare. The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning (a subset of artificial intelligence) plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data and records and the treatment of chronic diseases. As computer scientist Sebastian Thrum told the New Yorker in a recent article titled “A.I. Versus M.D., “Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful.”