P1846HISTORY OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATION IN NEPHROLOGY

Abstract

Abstract Background and Aims The application of artificial intelligence and neural networks in medicine is used to help solve problems that cannot be handled by the classical approach. The common name “cybernetics” encompassed the fields of management, information technology and biomedicine, but these disciplines continued to evolve independently due to the explosion of new knowledge. Over time, the development of neural networks has been turbulent and is now widely used in various fields of medicine and even in nephrology. The aim of the paper is to analyze the history of the development of artificial intelligence and its application in nephrology. Method Data were collected from books, magazines, encyclopedias and databases. Results Basic research on cybernetics and medicine was done by Golgi and Kelley doctors after Isaak Newton and Hermann von Helmholtz. The first theoretical mathematical models were derived in 1943 by Warren Mc Culloch and Walter Pitts. A few years later, a more contemporary contribution to the development of neural networks was given by Norbert Wiener and John von Neumann because they thought that research into biomedicine based on human brain function would be very interesting. In addition, in 1948 Norbert Wiener was the first to publish a work explaining the term cybernetics. At that time, the first experiments were made and new theories in the field of artificial intelligence were put forward by Marvin Misnki. The first training of neurons and the basis of all methods for training neurons was described by the Canadian Donald O Hebb. After the first successful neurocomputer in 1957, on which Rosenblatt worked, scientists have perfected various models of neural networks to this day. So far, mostly retrospective studies have been done in clinical nephrology, transplantation and dialysis with the help of algorithms used in neural networks. Particularly complex nephrologic patient relationships as well as assistance with timely implementation of new good clinical practice guidelines, patient prediction in at least the next month, and patient selection for palliative care are just some segments in nephrology that require the introduction of such tools into daily clinical practice with the aim of sensitive patient populations have better treatment outcomes, with physicians having more comprehensive insight and control over the mass of data. Conclusion Today‘s application of artificial intelligence in nephrology is based on retrospective research. The dizzying rise in technological development so far will allow the use of cybernetics and available tools based on neural network algorithms to enable and improve the nephrologists’ dedication and effectiveness.