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Machine learning (ML) is a subclass of artificial intelligence technology, where algorithms process large data sets to detect patterns, learn from them, and execute tasks autonomously without being instructed on exactly how to address the problem. Healthcare is an industry that keeps up with the times as well. With the amount of data generated for each patient, machine learning algorithms in healthcare have a great potential.
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Benefits of Machine Learning in Healthcare
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Smart Recordkeeping
It is challenging to keep a track of all the details of the patients and if the details are being updated everyday. This is because data entry is a monotonous task. However, it is also crucial for effective decision-making and better patient care. One of the uses of machine learning in healthcare is using optical character recognition (OCR) technology on physicians’ handwriting, making the data entry fast and seamless. This data can then be analyzed by other machine learning tools to improve decision-making and patient care.
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Drug Discovery and Production
Based on the previously acquired data on active components in drugs and how they affect the organism, ML algorithms can model an active component that would work on another similar disease. This kind of an approach can be used to develop a personal medication for patients with a unique set of illnesses or certain special requirements. In the future, this machine learning tool could be used in combination with nanotechnology for better drug delivery.
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Infectious Disease Outbreak Prediction
COVID-19 pandemic has shown us how unprepared we were to an infectious disease outbreak of this size. It is worth mentioning that experts in the area have warned the government about the possibility of such an event for years. Now, we have tools based on machine learning that can help to detect the signs of an epidemic early on. The algorithms analyze the satellite data, news, and social media reports, even video sources to predict whether the disease has the potential to grow out of control.
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Better Radiotherapy
One of the most sought-after applications of machine learning in healthcare is in the field of Radiology. There are many lesions, cancer foci, etc. which cannot be simply modeled using complex equations. Since ML-based algorithms learn from the multitude of different samples available on-hand, it becomes easier to diagnose and find the variables. One of the most popular uses of machine learning in medical image analysis is the classification of objects such as lesions into categories such as normal or abnormal, lesion or non-lesion, etc.
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Machine Learning in Decision Making
AI has played a very important role in decision-making not only in the field of health care, but also in improving businesses by studying customer needs and evaluating any potential risk that a business might face. A powerful use case of artificial intelligence in decision-making is the use of surgical robots that can minimize errors and any variations and will eventually help in increasing the efficiency of your surgeons. They help implement complex surgeries with better flexibility and control than any other approach.
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The Advantage of Machine Learning in Healthcare
1: Which of the following algorithms is most suitable for classification tasks?
There is a huge advantage of artificial intelligence and machine learning in healthcare. Some of them are as follows:
- It allows healthcare professionals to focus on patient care rather than spend time on information search or entry.
- There is a need for increase in diagnose accuracy. For example, machine learning has proven to be 92% accurate in predicting the mortality of COVID-19 patients.
- Using machine learning in medicine can help to develop a more precise treatment plan. A lot of medical cases are unique and require a special approach for effective care and side effect reduction.
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