The use of machine learning to supportthe therapeutic process – strengths andweaknesses

Abstract

Purpose: Artificial neural networks, "artificial intelligence" or machine learning now dominate a number of areas, making many activities automatic and thus affecting the safety and comfort of life. Neural networks might provide intelligent decisions with limited human assistance. Medicine also uses artificial intelligence, also in models designed to support the therapeutic process. The aim of this article is to define the main directions of development of machine learning applications in supporting the therapeutic processes. Views: Currently, the literature distinguishes at least a few applications of new technologies of varying degrees of advancement, with machine learning at the forefront [6]. It seems that the researchers are most interested in personalizing notifications of therapeutic applications, modifying therapeutic programs in a manner adapted to the patient's problems, and conducting "intelligent" conversations with them. Conclusions: There are dangers in using machine learning methods to support the therapeutic process. Particular attention should be paid to ensuring the full privacy of the implemented applications; moreover, selling user data of this type to third parties, such as those that sell certain medications or dietary supplements, would be ethically questionable. There are no legal regulations (or a system of recommendations of relevant scientific societies) that would limit proven applications to support the therapeutic process of a given disorder in the future, and which were created solely for the financial purpose of authors who did not conduct substantive consultations.

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