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Artificial intelligence and machine learning have already found application in many areas of human functioning. There are many indications that the healthcare system will also be improved thanks to modern technologies.

Blockchain and AI and health care

Same as technology blockchain, artificial intelligence is also becoming more and more important. It turns out that both technologies can be used in healthcare, but the question is whether the expectations associated with them are too high?

Of course, it should not be denied that they have numerous benefits for the healthcare system, but they should not be considered a panacea for all problems.

At least not yet.

Unfortunately, implementing blockchains in the healthcare system is quite a time-consuming process. Taking the American system as an example, not only there are many discrepancies in it, but it is also characterized by a multitude of input data and variables. Meanwhile, for it to be integrated with the blockchain, there must be full data compliance.

The same applies to artificial intelligence.

This does not mean, however, that these technologies will not be used in healthcare, quite the contrary. There are many indications that they will make it possible to introduce significant changes in this area. Both artificial intelligence and blockchain seem to set the path to be followed in order to legitimize the functioning of health systems, and thus ensure a higher quality of services. However, this process still requires some time.

Opportunities and difficulties

The concept of artificial intelligence is widely used today, and more and more often, due to such solutions as, for example, telemedicine services (which have gained special importance during a pandemic), it is also identified with the health service.

Nevertheless, the term is not always understood correctly, and is often treated as a synonym for machine learning, which is just one of many components of AI. Machine learning does not provide machines with ready-made instructions, but sets patterns for larger groups of data. Moreover, it is of great importance in his case that the created algorithms comply with the current knowledge, and this is not the easiest task. If the computer is "trained" incorrectly and the input data is not correct, the results may be inaccurate or distorted.

Artificial intelligence creates a powerful potential that is able to revolutionize the health service. However, everything is in the hands of specialists who design algorithms.

As already mentioned, the correctness of the results depends on their correctness. This issue is even more important in the case of health care - the price for a wrong protocol is much higher in this case than in the business sphere, because the health and safety, and sometimes even the lives of patients, are at stake.

Therefore, the implementation of AI in health systems should be based on communicating clearly expectations to scientists responsible for data, its purity and transparency, and on scalability.

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