- May 14, 2019
- Posted by: admin
- Categories: Health care, Machine Learning
Machine Learning in Healthcare
If you are any bit technologically aware, then you must already have heard about Machine Learning. Yes, machine learning is that part of the greater perspective i.e. Artificial Intelligence, which has taken the world today by a storm.
And in case you are wondering whether this can be of any use to the healthcare industry, then reading ahead will be worth your time.
In the world of medical sciences and healthcare industry, the contributions of Machine Learning is as expansive as anywhere else. More so because till a very long time, medical services involved a considerable percentage of human intervention. Starting from diagnosing ailments to carrying out tests and recording observations, everything was carried out using manual processes. But after technology took over, things have evolved to yield smoother and faster operations.
But simply speeding up the different aspects of healthcare services is not all, as companies are targeting to achieve greater accuracy in treatment to achieve a higher quality of patient satisfaction. So, with the existing technological processes in place, the entire setup was augmented with the benefits of Artificial Intelligence. And with the advent of AI and machine learning in healthcare, the whole scenario of this industry has now undergone a major overhaul.
It goes without saying that today Machine Learning is an integral part of the healthcare business. To understand why to let us look at the following points below:
Accuracy of inputs for a treatment
The benefits of machine learning in healthcare starts right from the basic levels, i.e. providing inputs to the doctors. With analytics and machine learning, the quality of inputs provided is much more accurate than before. No matter how complex the medical tests are, or how inconspicuous the symptoms might be, the level of accuracy achieved with ML is much higher today. This helps in a firmer diagnosis of health conditions that are highly time-sensitive, and on-time decision making to start with clinical treatments.
For example, cases as complex as diabetic retinopathy can now be detected using Machine Learning algorithms in the department of Ophthalmology something which widely surpasses the human grading efforts. Again, Stanford University scientists have generated deep learning algorithms for detecting skin cancer (source).
- Accuracy in patient profiling
Along with basic information such as blood pressure, machine learning can also be used for profiling a patient’s health condition. With machines incorporated with the newest AI algorithms, medical service providers can also get a ready summary of the patient’s susceptibility to stroke, kidney failure, and coronary diseases, along with with their family history, social status, updated clinical trial details and every other necessary information at one go.
While patient profiling is a sensitive aspect that involves various opinions, it is extremely important for the correct diagnosis and treatment of critical patients. Authentic profiling can also be a life-saver in case of many.
Reliability of diagnosis
With the help of supervised machine learning in healthcare, organizations are now coming up with an accurate diagnosis of the most complex ailments today. Advanced medical equipment, with the help of deep learning, can now be used to effectively detect abnormalities from even the minutest symptoms in laboratory test images. Departments such as pathology, radiology, cardiology, which are heavily dependent on image data, have benefited hugely from Machine Learning. The accuracy attained in these departments, by educating the machines with deep learning is far more objective and precise today.
For instance, Google has come up with a complex reasoning algorithm that is used to detect breast cancer from mammogram results. As per reports, this procedure produces results nearly 89% more accurate than that of a human (source).
Clearer estimates for patients
By now, you must be wondering if machine learning in the medical industry is only for the benefit of the healthcare provider companies and practitioners. While that is a logical question, it needs a different perspective to understand in entirety.
Machine Learning has benefited the healthcare industry as well as its consumers, i.e. the patients.
Deep learning enabled machines are designed to extract massive volumes of patient data and analyze them accurately. This accurate analysis offers a clear channel of clinical insight to the doctors, who can suggest suitable procedures for treatment. With the help of machine learning, different possible options of treatment can also be found, which thereby helps in choosing what works best for a patient. Patients can get a clearer understanding of what outcome to expect from a particular course of treatment, and the expenditure that is involved. And given that medical treatment expenses are sky-high nowadays, getting an estimate beforehand is always very helpful.
In a way, machine learning in the healthcare industry offers the much-needed clarity to common people, and also boosts cooperation among the doctor and patient. While it is not possible to eliminate the human touch in healthcare, surely Machine Learning can be used as an aid for doctors, to offer better facilities to ailing people.