Artificial intelligence And Machine Learning in healthcare

Machine learning ensures great help in medical science, it makes the use of the huge data out the in the field of medical examination & surgery and operations. Medical science learns from a large group of data released from research and development (R&D), physicians and clinics, patients, caregivers etc.

The saved data from big medical firms and the R&D department from various fields make sure to synchronize and use them for the treatments which would cost a fortune or at least save someone else’s life by bringing in some cost-saving measures. The entire idea behind this is the expertise is handled by the Machine and the AI which would be cheap and easier to get assisted with than the experts.

Introducing these methods and cost-saving methods for everyone’s use

As per a research, large amounts of data and machine learning would easily generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and some of the new sort of the tool creation for physicians, consumers, insurers and regulators. This is to make it more easily accessible as well.

Applications of Machine Learning and AI:

  • Disease Identification/Diagnosis
  • Personalized Treatment/Behavioural Modification
  • Drug Discovery/Manufacturing
  • Clinical Trial Research
  • Smart Electronic Health Records
  • Epidemic Outbreak Prediction
  • Radiology and Radiotherapy

These methods and cost-saving ideas are made to help those who can’t be assisted by the same surgeon or doctor at moments when they would really require the help. For, instance the doctor who specializes in Cardiovascular Surgery is in Cairo and a patient with a similar need of help is in Cape Town can get the data from the doctor’s previous experience and save his/her life.

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Written by Udit Agarwal

Startup/Tech News Correspondent, Responsible for gathering information on the tech companies working on IOT, AI, ML, Cloud, Mobile Technologies,

Udit can be reached at [email protected]

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