Top 15 Machine Learning Applications in Healthcare

It is near obvious how the emergence of technology has positively impacted the health sector today. From documentation of health records, down to the advanced level of treatment, medical applications, which are a subset of AI (Artificial intelligence), and other utility functions, the importance of these applications can never be overemphasized.

Here are the top 15 machine learning applications

1. 1. QUOTIENT HEALTH

      With this learning application, health sectors are able to reduce their health care costs because it was specifically designed to help in reducing the cost of supporting electronic medical records systems.

   2. KENSCI LEARNING MACHINE

      This machine learning in healthcare[1] application is being used for diagnosis purposes. That is, it helps doctors detect any form of illness in their patient’s body, that may occur in the nearest future. And through this, physicians are often able to prescribe preventive drugs that will destroy such illness before it grows.

   3. CIOX HEALTH MACHINE

      This machine learning in a healthcare application helps facilitate the storage and spread of health data from one facility to the other.

   4. PATHAI

      This machine is mostly used by the pathologist to instantly identify an illness in their patient, and then decide whether or not such patients would need a new form of therapy or continue using drug treatment.

   5. QUANTITATIVE INSIGHTS

      With its MRI workstation Quantx, this machine quickly spots out breast cancer most especially. It is one of the most popular applications used in treating cancer.

   6. INNEREYE (MICROSOFT)

      This application helps physicians to distinguish between healthy anatomy and tumors, through its 3D radiological displayed photos. Plus, it also helps surgeons plan their surgical operations accurately.

   7. PFIZER

      This machine, through the help of IBM’s Watson AI technology, helps physicians by providing ideas to how one’s body immune system can fight off cancer.

   8. INSITRO

      Insitro helps in developing low cost but high-quality drugs for patients who need urgent treatment but can’t afford the luxury of paying for expensive medication.

   9. BIOSYMETRICS

      Through Biosymetrics learning application interface, medical practitioners are able to process data quickly, and thus save them time. It can be used in healthcare sectors such as biopharmaceuticals, technology, and other health facilities.

   10. CONCERTO

       This machine learning healthcare[2] application is mostly beneficial to oncologists and pharmaceutical companies because it provides an accurate analysis of oncology data to health workers.

   11. ORDERLY HEALTH

       This application serves both as an intermediary and a guiding angel. Intermediary because it helps patients understand what benefits are under any health package. And an angel, in the sense that it links patients to the right health care facilities for treatment.

   12. MD INSIDER

       Unlike Orderly health, this machine learning application focuses only on matching patients with the right medical practitioners.

   13. BETA BIONICS

       Through its bionic pancreas known as iLet, this machine helps in monitoring blood sugar levels in patients with type 1 diabetes.

   14. PROGNOS

       With its acclaimed registry of 19 billion records for 185 million patients’, Prognos helps in diagnosing diseases, suggest when a therapy is needed, alongside other health assistance.

   15. BERG

       This machine is mostly used to spot out diseases and subsequently give treatments, neurology, oncology and other unpopular forms of diseases in the body.

In a nutshell, machine learning in healthcare has tremendously raised the standard of medical administration from the meandering, time-consuming mode of operation, to a simple, quicker and more accurate bureaucracy.