Leveraging Technology for a Better Future
If you have data, there’s a lot of artificial intelligence which can work pretty close to human sensibilities in order to bring about awesome outcomes. What is central though is the presence of humongous quantities of data for the systems to whet its teeth on.
With most fields of activity generating their own share of data, there’s always scope for AI to work out certain outcomes. Of these, the most significant use is in the field of healthcare where data gets captured across various points for various purposes, be it to record diseases, accidents, cures, duration of disease and its cure, signals given by our bodies and so on.
In sum, putting to use such humongous data when put in the right silos besides giving better insights into diseases and their cure, shall also greatly help allied industries, as the likes of wearables.
Below are specific instances where, the healthcare industry has found the right medium of cures and cares, by leveraging AI and Big Data.
Better interpretation for early detection.
Neural networks like the ones developed by the likes of Google to identify traffic signals and stop/start vehicles led to the thinking that it may be possible to use the same kind of intelligence to study retinas of diabetics to detect the possibility of early-stage blindness which they may suffer later in life. These aside, AI and deep learning using their amazing neural networks can detect anomalies in x-rays and MRI scans at speeds unthinkable for human radiologists.
Reducing costs associated with dispensing medical assistance
On an average, 25% of the entire cost of providing medical assistance goes to administrative costs which includes compensations and remuneration to medical assistance staff. This entire cost which may amount to a whooping USD 150 billion in a few years’ time can be entirely done away with and the savings transferred towards better services by depending upon AI-related systems. Another aspect is to transfer data collection to AI-assisted self-care systems which can be handled by patients themselves.
Reducing costs associated with discovering and producing drugs
Without the backing of AI and related big-data, therapeutics, the field of discovering new drugs, uses and ways of producing drugs could take years to come about and that too without any guarantee of success to recover the resources spent. Wrong set of data, incomplete data etc. are some of the shortcomings involved.
Estimating likely outcomes without a fast system of computing usually takes years to come to a conclusion, where everything including testing has to be done manually. All these can be obviated where ML systems using correctly siloed data speeds up the process of drug discovery. While years can be shaved off using an ‘in-silico’ approach instead of ‘in-vitro’ and ‘in-vivo’, the predictability of outcomes also become better. All this though depends upon the availability of significant quantities of data and information.
This edition Most Reliable Diagnostic and Pathology Centres to Watch, places the spotlight on healthcare service providers that are leveraging such revolutionary technologies to deliver the best to those seeking their services.

Author's Bio: 

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