Intelligent Machines Are Better At Health Diagnostics Than Humans

As Australian, British, and American colleges continue steadily to graduate more and more medical students, the apparent question is where will these new doctors to work in the foreseeable future? Will there be an extended role for medical professionals due to our aging populations? Or is pressure to lessen costs while improving outcomes likely to force the adoption of new technology, which will likely rot the variety of roles currently performed by doctors then? All national governments, patients, and doctors throughout the world know that healthcare costs will need to reduce if we are to treat more folks. Some propose making patients pay more, but we pay for it however, it’s clear that generating the price down is what needs to happen.

The use of medical robots to assist human surgeons is becoming more popular but, so far, they are being utilized to improve patient outcomes and not to reduce the price of the surgery. Cost benefits may come when this robotic technology matures later. It is in the area of medical diagnostics, where many people see the possible significant cost reduction while enhancing accuracy by using technology instead of human doctors.

It has already been common for blood tests and genetic screening (genomics) to be carried out automatically and incredibly cost effectively by machines. They analyze the bloodstream specimen and automatically produce a record. The tests are often as simple as a hemoglobin level (blood count) to tests of diabetes such as insulin or sugar levels. They can also be used for a lot more complicated tests such as looking at a person’s genetic makeup.

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A good example is Thyrocare Technologies Ltd in Mumbai, India, where more than 100, every evening 000 diagnostic tests from around the united states are done, and the reports delivered within 24 hours of bloodstream being extracted from an individual. If machines can read bloodstream assessments, what else can they do? Though many doctors shall not like this thought, any test that requires pattern recognition will eventually be done better by a machine than a human. All of these examples, and in fact all pathological diagnoses are made by a doctor using pattern recognition to look for the diagnosis. Artificial intelligence techniques using deep neural networks, which are a kind of machine learning, may be used to teach these diagnostic machines.

Machines learn fast, and we aren’t talking about an individual machine, but a network of machines linked via the internet internationally, utilizing their pooled data to keep improving. You won’t happen right away – it will take some time to learn – but once trained the device will only continue to get better. With time, an appropriately trained machine will be superior at design acknowledgement than any human could ever be.

Pathology is currently a matter of multi-million-money laboratories relying on economies of level. It takes around 15 years from departing high school to teach a pathologist to function independently. It probably takes another 15 years for the pathologist to be as effective as they shall ever be. Some full years after that, they’ll retire and all that knowledge and experience are lost. Surely, it would be better if that knowledge could be captured and utilized by future generations?