Across the globe, various hospitals are using sophisticated technologies and processes with the help of highly skilled administrators. But that ain’t an easy task to accomplish! The management of such high-end machinery can still be a supervisory nightmare for healthcare professionals who already have time constraint issues.
Only automation can sprinkle wonders here. There are certain routine tasks which only Machine Learning can mechanize like automatic billing, documentation and administrative processes.
With the advent of automation, it has also become easier to diagnose ailments and treat patients in a better and efficient manner. We can safely connote that Machine Learning is here to transform the healthcare industry along with other associated industries such as medicine, pharma, etc. Let us explore how Machine Learning has revolutionized the various segments of the healthcare industry.
Helps in radiotherapy and radiology
Various scans like CT and MRI tests conducted for radiological investigation and analysis are pretty cumbersome and time-consuming. They help in determining the distinction between infectious and healthy tissues – which when done by humans bear a good chance of getting errors and wrong diagnosis. It is here that machine learning comes into the picture. Machine Learning algorithms can help in expediting the process and drive precision in radiotherapy procedure.
Machine Learning can furnish medical practitioners and physicians with effective information which may aid in better diagnostics and accurate tumor detection. Besides, ML can also help in predicting the radiotherapy response for creating a personalized treatment tracker. Of late, Machine Learning algorithms are also being used for medical image analysis and deriving medical information to bring forth the accurate diagnosis of medical conditions.
As a matter of fact, Deep Learning is also being used for capturing lung cancer growth through CT scans which are able to screen the disorder at the initial stage and provide effective solutions for medical use.
Aids in ailment detection and drug discovery
Various healthcare systems generate an ample quantity of data and information for ailment prediction. But this is not it! A huge amount of unstructured data are unable to provide meaningful insights into the actual disorder.
For this purpose, Machine Learning algorithms are used to detect the footprints of diseases on a massive scale. This is done by allowing the systems to learn and make certain predictions on the basis of some processed data which was previously available.
These predictions can be used for analyzing the chemical compounds which can ultimately aid in drug discovery. By doing this, a lot of time can also be saved during experimentation and repetitive work can be eliminated. With the use of Machine Learning algorithms, it has comparatively become easier to detect symptoms of the critical disease and the probability of survival has also gone up. In a nutshell, ML boosts the existing treatment facilities and processes.
For example, by using Machine Learning algorithms such as predictive analytics, IBM has joined hands with Teva Pharmaceutical to explore new treatment options for the central nervous system and respiratory diseases which run on IBM Watson Health Cloud.
Use of precision medicine
The latest trend is of Precision Medicine which primarily revolves around healthcare practices which are specific to a particular patient. So what does it comprise of? This consists of analyzing a patient’s hereditary information, health history, environmental conditions, and preferences to diagnose the actual disease and provide subsequent treatment.
In this scenario, Machine Learning algorithms are used to analyze the patient data and identify crucial factors such as the genetic history and predilection to diseases. This could help in strongly determining the actual rate of treatment success or failure.
However, Machine Learning techniques also help in exploiting the genomic and molecular data which directly help the doctors to provide better treatment facilities to patients and understand the disease mechanisms to the core. Further to this, ML also helps in detecting the diseases which are likely to occur in the near future and recommend solutions to keep those ailments at bay.
Upgraded hospital management system
Machine Learning is like a new tide which could bring about a major change in a traditional hospital management system. This could be done by automating the regular routine tasks like admission and billing procedures in hospitals, close monitoring of patients innards, administrative processes, etc.
With the implementation of ML techniques in hospitals and medical centres, it has comparatively become easier to track the patient’s real-time location. Machine Learning techniques like Predictive Analytics assists in furnishing the ongoing stream of real-time reports of patients’ health conditions. In addition to this, ML can also be used to automate non-medical operations, which may include housekeeping, laundry, pharmacy, and food delivery.
Considering the current digital health generation primarily focuses on wellness and good health rather than medical ailments or disorders. We may connote saying that the inclusion of Machine Learning in the healthcare industry provides an enhanced patient experience, improvement in public health care management, which in a way helps in the reduction of costs and expenditure by automating human labor.
We are currently in a revolutionary era wherein the next step in healthcare is the successful partnership of medical practitioners, physicians, and doctors with the machines.
This would bring forth an innovative revolution with an efficient, precise, and upgraded care and treatment in the healthcare landscape.
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