Artificial Intelligence (AI) has taken over several tasks of human beings in today’s modern times. Supported by rapid technological advancements, AI gives machines the thinking and problem-solving ability like us humans, making our lives a lot easier and saving a lot of our time.
Be it speech recognition or an army of diligent robots serving scrumptious dishes to customers at a hi-fi restaurant; AI is present almost everywhere. While AI appears to be the generic name given to such human-like qualities installed in machines, it has also given birth to several other branches. One such well-known branch of AI is Machine Learning (ML). This subset of AI has been gaining rapid popularity since the past few years, with continual developments in its application.
To be specific, the courtesy of Machine Learning has enabled computerized systems to do several tasks without being manually controlled by human beings. Machine Learning teaches such high-tech systems to perform tasks based on past examples, set instructions, or direct experience with new data sets. This handy AI subset aims to allow computerized systems to be able to produce and predict accurate results.
Besides being used in multiple fields, doctors, clinicians, and medical researchers are seen to be taking full advantage of this technological breakthrough.
Aiding Medical Diagnosis
Machine Learning is widely used in diagnostic centers and clinics. The machines set up to diagnose and test patients are all familiar with the necessary conditions that are useful in identifying a particular illness stage. It uses complex algorithms to come to a concrete conclusion, after carefully describing the relationship between variables.
Ever experienced running your hands through a messy drawer stuffed with random items? Let us say you need your pair of car keys from this clumsy drawer of yours. You cannot see them, but still, somehow, get your hands on the keys. But how could you do this so thoroughly? You could because of merely remembering the correct placement of it. Thus, despite not being able to see through your messy drawer, you still managed to get them out.
https://www.pexels.com/photo/men-checking-on-the-computer-3913019/
Machine Learning helps to train the machines precisely in this same manner. The diagnostic machines become familiar with the pathogen due to being introduced to it before. Humans do this primary introduction, but the machine gradually becomes more familiar to the pathogens due to identifying them multiple times during medical tests. Over time, the machines gain proficiency in identifying various diseases, fulfilling ML’s aim of their self-development, and improved accuracy.
AI Subset Decoding Disease Subtypes
Machine Learning has made disease diagnosis a lot less time consuming than it would have been in its absence. Imagine doctors having to manually tackle all your samples and match them with the identified disease nuclei. That would have taken days to carry out a medical test of just one individual.
Besides saving us time and effort, Machine Learning has also helped medical professionals to decode ancient medical reports. ML, combined with Natural Language Processing (NLP), has helped medical researchers a great deal in understanding and converting past medical reports. The stated diseases and their identifying features have been used to set up the modern diagnosis machines, making the tests quicker and accurate.
Machine Learning has also lead medical researchers to identify the childhood phenotype of asthma. Assessed data from the Manchester Asthma and Allergy Study (MAAS) was carefully incorporated into the computerized systems, only to figure out clusters of asthma causing pathogens. This accurate cluster analysis via Machine Learning is believed to detect the risk of childhood asthma more efficiently.
According to professionals, this data-driven technology can be used to diagnose multiple diseases and their various subtypes, resulting in precision medicine advancement.
Machine Learning Leads to Perfect Results
Machines may lack the ability to evaluate varied situations like us humans. But its prime advantage over us is its quality of pinpointing even the smallest of errors. Besides, its ability to present exact results with proof can never be obtained by human doctors.
Machine Learning has helped to lead the medical field a lot further than human doctors could. It is not mere convenience, but a necessity to this particular field, like many others. Imagine a patient having a tumor in a certain part of their body. The tumor would have to be examined first, in order for the doctors to decipher whether it needs to be operated or not.
Without Machine Learning, it would be impossible for human doctors to tell how the tumor looks and its dimensions. It would also be out of their capacities to state whether it is malignant or benign. Hence, the special machines with set instructions and identifiable, past data records help to solve such problems.
The machines are well equipped to produce pictures of the tumors. But how do they understand whether the tumor is cancerous or not? They do this by analyzing the pixels of the produced picture. The pixels display the tumor’s size and type, letting the machine understand its medical state.
The above statement of examining inner tumors is just a mere example of how Machine Learning is important for doctors to properly carry out their treatments. Several other health issues like cysts, ulcers, organ inflammations, blood clots, and infections can easily be detected via Machine Learning.
If Machine Learning had not been in practice, then such inner health issues could never be discovered. It certainly has a big hand in increasing the global population’s lifespan through its proper detection of diseases.
Conclusion
To sum up, medical data has helped doctors and every individual associated with the medical field. Breaking down the various treatment techniques stated in the past research reports are in use even today. The thousand-year-old methods combined with today’s cutting edge technology are the driving forces of the advancement of the medical field.
Machine Learning has surely come a long way, putting forward a helping hand to the entire medical field. Another way in which this field could be aided is via medical surveys. Such surveys can act as a great source of awareness for people. Besides, it can be a life-saving movement. Participating in these surveys could let people know of their health conditions better.
Through informative surveys, they would be better informed about lesser-known diseases. Thus, they are more likely to seek medical help at an early stage of their illness, ultimately saving their precious lives.
About The Author: Elisabeth Andrew is a freelance healthcare writer. She is passionate to write about women’s health, side-hustle for doctors, medical surveys, healthcare technology and machine learning in healthcare.
