A transformational edge: Use of machine learning applications in healthcare system

The industry of healthcare promotes the welfare of the people by providing value-based care and earning good revenue in the process. It runs on three focal points namely, quality, care, outcomes which makes its future promising enough. All over the globe, the medical professionals and teams are working in best potential to deliver the promise made towards society. The population burden has risen along with the entry of internet-connected medical devices that hold the healthcare system, quite efficiently, making it a normal thing for such devices to exist in the day-to day operations. The technology enabled smart healthcare is here and now, not a thing of the future anymore. 

The technology entirely permits the healthcare professionals to develop alternate staffing models, IP capitalization, provide smart healthcare and reduce administrative and supply cost. The introduction of machine learning in healthcare has been widely accepted and acknowledged by people all over the globe. Few instances where machine learning is playing tremendous role are: 

  • Developing a machine learning algorithm by Google to identify cancerous tumors in mammograms.   
  • Using deep learning to identify skin cancer at Stanford University. 

Applications of machine learning in healthcare are as follows: 

 

Identification of disease and diagnosis:

The first and foremost application of machine learning is the diagnosis of disease and illnesses which are quite complicated to diagnose without its use. It may comprise cancer which is not easily detectable in early stages or any other genetic diseases. An example of how cognitive computing when combined with genome-based tumor sequencing can help in providing diagnosis at a faster pace. Yet another biopharma giant known as Berg is utilizing AI for the development of therapeutic treatment in oncology and other areas. Another application known as P1 vital’s predict ( Predicting response to depression treatment) is playing a chief role in developing a commercial yet feasible way to diagnose or provide the right treatment to patients in regular conditions. 

 

Discovery of drug and its manufacturing:

machine learning is also incorporated to be used in the early stage of a drug discovery procedure. It generally includes the R&D technology, namely next-gen sequencing and precision medicine whose main function is to find alternatives for carrying out therapy of multifactorial diseases. In the current scenario, the machine learning processes and techniques involve learning that is unsupervised which helps in identification of data patterns without having to provide any predictions. Another profound project named as Hanover by Microsoft is engaging the use of machine learning technologies for conducting the process of various initiatives such as development of AI-base technology for treating cancer drug combinations for AML. 

 

Diagnosis through medical imaging:

the breakthrough technology called Computer vision is the combined result of machine learning and deep learning. It was utilized and acknowledged by Microsoft in one of its initiatives, Inner Eye that works on wonderful image diagnostic tools for doing image analysis.now machine learning is becoming more accessible with time and expected to grow in its capacity that might bring good news in medical imagery soon, if it works well. 

 

Personalized Medicine:

personal treatment works the best in combining individual health along with the predictions. Moreover, it leads to further research work and better assessment of the disease. In the current times, there is not much to study for the physicians, so they analyze on the basis of the specific set of diagnosis or estimate the risk for patients based on symptoms and any other genetic information. But with machine learning comes various treatment options for the physicians. Hopefully, in the coming future, we will get to witness more devices and biosensors with sophisticated health measure capacity that will allow more availability of data for physicians and healthcare professionals.

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