Artificial intelligence to change a doctor’s job and to compensate for the shortage of resources in healthcare

Healthcare has reached a turning point. With public healthcare in Finland and elsewhere in the world suffering from a shortage of resources, the possibilities offered by artificial intelligence can, in the future, open entirely new vistas in healthcare. Although the image of care robots may seem clinically cold compared to human nurses, in reality, artificial intelligence can free the staff’s time for engaging with patients.

What kinds of practical possibilities can artificial intelligence bring to healthcare?

Klinik Healthcare Solutions is a pioneer in bringing together healthcare and artificial intelligence, in Finland and around the world. An online application designed by Klinik frees up more time for nurses in direct patient care , as first contact with a healthcare facility is made through a browser-based service running on a medical algorithm.

Furthermore, artificial intelligence has a variety of other potential uses in healthcare. Human memory is limited, and the human brain cannot handle the same amount of data as a machine. Indeed, a doctor’s job description is likely to change in the future, and even be updated to a new level. In the future, a doctor will, prior to meeting a patient, have access to effectively analysed data on them. This is in contrast to today’s doctor’s appointments, in which there is no time or opportunity to search for this type of data. The final diagnosis is a combination of analysing the preliminary data and meeting the patient. Diagnoses are faster and more accurate when a machine takes care of the time-consuming preliminary work for the human. The change could be compared to the time when office workers were first given computers to use. The need for human labour will not go anywhere, but certain routine tasks will be handled more effectively. The doctor, in turn, will be left with more time to the genuinely engage with the patient.

The doctor will be left with more time to the genuinely engage with the patient.

What will also change is how patients use the services provided by healthcare organisations. It will no longer be necessary for patients with minor ailments to physically go to a healthcare centre, but it will be sufficient just to submit a photograph of, for example, a wound, mole or rash. The use of machine vision in imaging enables a fast and accurate review of results. A machine stands a good chance of learning how to make reliable identifications from images, as long as there is sufficient data as a basis from which it can learn to perform its tasks. The biggest challenges lie in finding enough reliable data to form the basis on which the machine can be taught.

The biggest potential of artificial intelligence is in the analysis of large amounts of data. Healthcare organisations may already hold large amounts of data on specific demographic groups, but its effective analysis is too great a task to be done using human labour. Data analysed using machine intelligence can, however, elicit certain regularities that will help us understand the incidences of illnesses and even prevent them.

Artificial intelligence will help to shorten queues and to ensure that anyone needing treatment will receive it.

The main promise of artificial intelligence currently is that it will enable more and more people to get the treatment they need. It is impossible to compensate for the shortage of resources in healthcare by means of human labour, when scarcity prevails. Artificial intelligence will help to shorten queues and to ensure that anyone needing treatment will receive it.

The development of artificial intelligence at Klinik pays particular attention to quality and reliability. One important feature about the care recommendations generated by artificial intelligence is their transparency and auditability. Whereas human errors are part of life, no errors are permitted for machines. The development of Klinik’s medical algorithm is the responsibility of a group of doctors specialised in machine learning.

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