Future Aspects Of Machine Learning In Health Sectors

  These are some of the few areas where machine learning can be induced to present better information regarding health sectors.

 1. Information Management

There is a constant rate of change in how the technology changes. As it has been said in Moore's Law that every second year, the number of processors in an integrated circuit will double, resulting in more Computer processing power. 

Such rapid fast processing has led to the development of machine learning within Computers leading to the development of Artificial Intelligence. 

Image Via Google

As time proceeds. Many of the health care sectors will have improved Database systems and well as have Efficient DBMS which would lead to an increased and effective information Management within the hospitals, Clinics and Pharmaceutical companies.

2. Clinical Decision Support System 

As of right now, Majority of the decisions are carried out by doctors who have to go through a hurdle of obtaining the right information for a certain patient to carry out diagnosis with other health professional. 

Image Via Google.

The presence of machine learning can help reduce such painful process of diagnosing individuals. 

Machine learning can be composed to learn about different responses for certain disease diagnosis by having a health professional Input the data within the system so that AI can use the data saved within machine learning to give a diagnosis and prove efficiency in decision making

3. Pain Management for Patients.

The use of Anesthesia has been the most prevalent and successful form of controlling pain in patient going through intensive surgeries since 1865. Semi-drug based components like Morphine are also introduced to reduce live pains. 

These drugs in itself affect the nervous system as they induce systematic pleasure or numbness to pain. so how can we AI help over this in future health care ? 

Image Via Healthsector.ca

Machine learning can be done to improvise a sense of virtual reality to prepare the individual or distract his "Senses". To manipulate the nerve sense into tricking that the patient is in another Biome. Find ways to connect AI with internal receptive signals to detect and identify for symptoms of depression or elevated anxiety. 

4. Integrating Human-In-the-Loop Machine Learning (HIl ML) for smooth transitions

In this scenario, Health professionals and workers can input the "Daily experience" they face into a suitable database through which AI can make decisions by data mining and running analytical process to determine best data which it can learn directly through humans. 

Image via Wired

Just like in practical life, The best way to learn is through experience and the experience of those professional within the field can give us the best data and scenario learning for machine learning.

5. Efficient Drug Discoveries using Simulations. 

2020 is the year that has truly demonstrated the importance of AI within Health sector. Due to the presence of these Machine learning AI, we were able to gain progressive results in the development of sarsCov-19 Vaccine. The simulations were carried out by Pfizer and Biontech on general population and by the end of the year, almost 90% vaccine success rate was observed in overall population range. 

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With such success in establishing a quick vaccine almost within an year (or 2), we can question the essence of AI to help find us the cure for un-curable diseases like cancer, herpes, AID's.

We can Integrate simulations based on each of these diseases and derive possible scenarios resulting in the development of cure.