LEARNING PROCESSES IN ARTIFICIAL INTELLIGENCE TECHNOLOGIES
DOI:
https://doi.org/10.11603/mie.1996-1960.2024.1-2.14993Keywords:
learning processes in artificial intelligence technologies, conceptualization, supervised machine learning, semi-supervised learning, classification algorithms, self-supervised machine learning, reinforcement learningAbstract
Background. Existing models of learning theory are discussed and a new transdisciplinary model is proposed for modeling the role of artificial intelligence (AI) in improving learning processes and decision-making procedures. The purpose of the study is to conceptualize the fundamental ideas for the model, which can become the basis for the development of artificial intelligence applications to support learning processes at various levels of knowledge transfer.
Materials and methods. Theoretical analysis, generalization and systematization of research re- sults were carried out using the leading scientometric databases PubMed, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Web of Science and Scopus. Methods of content analysis and knowledge engineering were also applied.
Results. Random use of the teaching method cannot ensure high results of knowledge transfer — a well-founded selective strategy of knowledge transfer is extremely important. Machine learning algorithms are divided into six broad categories: supervised learning, semi-supervised learning, un- supervised learning, self-supervised learning, and reinforcement learning, multi-instance learning. An important challenge in new learning processes is solving moral and ethical problems.
Conclusions. The development of high-quality and reliable simulation environments for practicing knowledge transfer algorithms in health care can facilitate the development and validation of training methods beyond the limitations of retrospective studies. The application of methods tested in such simulation environments in real clinical conditions with simultaneous supervision by clinicians will allow solving the problems of algorithm selection.
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