Machine Learning (ML) is an integral part of Artificial Intelligence which has garnered a lot of attention in almost every field. Whether it is critical operations like machine learning in healthcare or implementing technologies or modes of recreation like gaming, ML has established its prominent position and role in the true sense.
In this article, we discuss how applying ML machine learning in healthcare is going to transform the industry and improve the medical field massively. Its influence is going to be major and something that should not be ignored.
The Applications of Machine Learning in Healthcare:
A machine learning algorithm is principally developed in a way that it learns to produce outputs gradually from the data points that are fed to the system and it utilizes techniques like regression, classification, artificial neural networks, deep learning, vector machines, etc. to process the data and produce the most viable output.
The following are some of the top applications of machine learning in healthcare that show a lot of potentials to create wonders in the field.
Enhanced Diagnosis for Improved Healthcare
One of the major challenges to treating any ailment is its speedy and correct diagnosis. Machine learning in healthcare is able to identify diseases and ailments in their early stages like cancers, diabetes, cholesterol, or other hereditary diseases. ML and AI together have shown massive progress in the field of oncology, psychotherapy, and others for fast diagnosis, all along with reducing the costs for tests.
There are many IoT-based devices that are now available in the market like Fitbit among others that monitor and display the heart rate and track its well-being. For patients with fluctuating sugar levels, there is a fixed pattern in the body that can be easily identified with the help of data science and machine learning. It can bring a hidden problem into a clear picture as well.
Surprisingly, ML has become so advanced that it can help identify Parkinson’s disease or Asperger’s syndrome with sufficient data gathered over time. This technology is revolutionary in a way that identifies problems that go unidentified and the patient suffers in silence.
Machine Learning in Healthcare for Drug Research
There is an overwhelming amount of research and data being carried out by scientists, medical experts, etc. It is almost impossible to keep a tab on each of these discoveries or developments. Often certain case studies might go unnoticed and can pose a loophole in the research being carried out.
The single solution to all the problems is implementing machine learning that gathers and processes hundreds of thousands of medical data, documents, feedbacks, and findings to improve the treatment options.
Machine learning speeds up the entire process and predicts the possibilities of side effects or various test cases. It also gathers the feedback of patients and various clinical trials and reduces the data that researchers have to actually deal with consequently creating precision medicine.
Robotic Surgery for Higher Success Rate
While automation in surgery is nothing new, introducing machine learning in surgeries guarantees higher precision and finer details in the operation. In case of tighter spaces and situations where there is absolutely no room for trembling hands, robotic surgery can serve as a boon.
The ones who keep up with the knowledge of healthcare systems advancement must be familiar with the Da Vinci robot which has showcased accurate results and is pathbreaking as compared to humans. Other important surgeries include dental, hair transplants, bone replacement, etc.
The robotic surgery application can be divided into the following parts:
Automatic suturing
Surgical skill evaluation
Improvement of robotic surgical materials
Surgical workflow modeling
Image Analysis or Medical Imaging
Machine learning has enabled us with what can be called “Computer vision”. A very significant development in machine learning services is the analysis of skin images for identifying skin cancer. The tools have proven a phenomenal accuracy of nearly 95%.
Machine learning is making great strides in the area of pathology and radiology. It has proven to be helpful in identifying myelogenous leukemia (AML) and other skin related cancerous tumors. It is helping largely in providing personalized treatments to the patients and also predicting whether the particular treatment method will prove to be successful and responsive by the particular patient’s body under the present conditions.
Electronic Health Records for studying Patterns
Machine learning in healthcare and deep learning is very important considering the prevailing risks to people living in a certain region/geography, etc. Moreover, maintaining the complete medical history of an individual helps analyze the symptoms, identify the cause, and provide a suitable treatment.
When such records are fed to a well-devised machine learning algorithm, it assures physicians of the treatment they are implementing. It also makes a cumulative study of the drugs that show results and discontinue the ones showing a negative impact or side effects.
EHR/EMR is helping study the genetic behavior of certain diseases and help make predictions to take preventive measures to avoid those defects to be carried in generations to come. It is a major contribution to the field of medical science.
Parting Words:
In addition to all these applications, extensive work is being carried out for the following:
Outbreak Prediction
Clinical Trial and Research
Handling administrative tasks with Natural Language Processing
Accelerating medical research.
Thus, there is no denying that machine learning in healthcare is a boon that holds a lot of potentials. Its capabilities are being identified and data scientists, analysts are working in synergy with experts in the medical profession to treat diseases, improve longevity, and impart a life that is healthy and disease-free.
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