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What are the real-world benefits of machine learning in healthcare?

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What are the real-world benefits of machine learning in healthcare?

Healthcare domain is regarded as a valuable industry that stands out of the ordinary in providing top quality of care to a wide array of people across the globe. It is also considered to be one of the best revenue earners across the world for several centuries. At present, this industry is earning almost $1.668 trillion in revenue. The country is also spending a lot of money on the healthcare domain, in comparison to other countries. Technology enables services to have also become an integral part of the healthcare industry.

Here is a list of some of the prominent benefits of machine learning across the healthcare industry:

Identification and diagnosis of diseases

One of the top applications of machine learning in the healthcare industry is the diagnosis and identification of diseases that were once very challenging to diagnose. It is inclusive of certain cancers which were hard to be diagnosed during the first stages, as well as several genetic diseases.

Discovery and manufacturing of drugs

One of the top benefits of machine learning is the discovery of drugs in the early stage. It is also inclusive of research and development technologies like a precision machine, next-generation sequencing which is beneficial to look for alternate techniques for the therapy of different multi-factorial diseases. At present different machine learning techniques are inclusive of unsupervised learning which plays an integral role in the identification of different data patterns without offering any sort of predictions.

Medical imaging diagnosis

Deep learning and machine learning help in the breakthrough technology, referred to as Computer vision. They are known to have been accepted widely in the InnerEye initiative, which was produced by Microsoft. The company is working on a wide array of image diagnostic tools that facilitate image analysis. As the machine learning is becoming accessible on a wide scale and they are growing with immense capacity, a wide array of data sources is expected to be seen from different medical imagery as an integral part of the AI driven process.

Personalized devices

Customized treatments will prove to be more effective by the pairing of individual health along with predictive analytics. It is also useful in the assessment of diseases effectively. At present, the health care professionals have to select from a certain set of diagnostics. They can perform an estimation of the risks of the patient, following the available generic information and symptomatic history. Machine learning in medicine is earning a high reputation.

IBM Watson Oncology seems to stand ahead in this movement as they are making use of the medical history of the patient for the generation of a wide assortment of treatment options. Thus, in the next few years, you are going to find a bunch of biosensors and devices that feature a plethora of sophisticated health care facilities.

Behavioral modification based on machine learning

Behavioral modification contributes to being a crucial part of preventive medicine. After the introduction of machine learning in healthcare sector, a wide assortment of startups has come up in the field of cancer identification and prevention, and treatments, on a wide scale. A wide array of analytics companies are using machine learning for the recognition of gestures by which it is possible to gain an understanding of the unconscious behaviors as well as introduce the required changes.

Smarter health records

Maintenance of health records daily is an exhaustive process. Though technology is playing a major role to make the process of entry of data easily, a wide array of processes are taking a huge amount of time for the completion. The goal of machine learning in the domain of healthcare industry is easing the process for saving efforts, time and money.

Document classification processes are making use of machine learning OCR and vector machine techniques. Machine learning plays an integral role in the creation of smart and intelligent health records that will be incorporating machine learning tools, for the diagnosis and clinical treatments.

Clinical trial as well as research

Machine learning has immense and potential applications in the domain of clinical trials as well as research. Clinical trials require a lot of time and involve a huge cut off from the pocket. Application of machine learning predictive analytics is useful in the identification of different clinical trial candidates. It is also beneficial to the researchers in drawing a wide array of data from a bunch of data points like social media, past doctor visits, etc.

Machine learning also finds wide use in ensuring data access and data monitoring in real-time. It is also useful in choosing the best ever sample size for testing. It also reaps the benefits of electronic records for the reduction of data-based errors.

Crowd-sourced collection of data

Crowdsourcing is considered to be an integral part of the medical domain these days. It helps the practitioners and researchers to get access to a wide assortment of data and details that are uploaded by end-users. The live health data provides greater ramifications. The ResearchKit of Apple helps the end-user in getting access to the interactive apps which make use of machine learning-based facial recognition for the trying and treatment of Asperger’s and Parkinson’s disease.

As the latest advancements are made on Internet of Technology, machine learning company is introducing several positive changes in healthcare industry. It brings an improvement in the diagnosis as well as medication.

Better radiotherapy

One of the major applications of machine learning in the domain of healthcare industry is the Radiology field. The algorithms which are machining learning based are capable of learning from a vast array of samples which are available on-hand.

Machine learning is used on a wide scale in medical and billing, patient care, etc. It is also used by the healthcare specialists on a wide scale for developing IP capitalization, alternative staffing models, offering smart healthcare to the patients and reduction of supply and administrative costs. Machine learning is getting wider acceptance across the healthcare industry. Machine learning algorithm is used on a wide scale for the identification of cancerous tumors in the mammograms. Different researchers are making use of deep learning for the identification of skin cancer.

Associate Editor

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