Application of artificial intelligence-based tools in experimental and translational cardiology: a review

Abstract

This literature review systematizes and critically analyzes artificial intelligence (AI) tools used in experimental and translational cardiology, assessing the prospects for their practical use and the corresponding limitations. Currently, AI tools are most actively used to automate the search, systematization, and analysis of information, as well as to perform mathematical calculations during the experimental planning stage. Research on AI algorithms for automated analysis of text and graphical datasets based on machine learning is aimed at reducing the time required for preclinical studies to accelerate the translation of cardioprotective, anti-atherosclerotic, endothelial-protective, and anti-calcification pharmacological interventions into clinical practice. In particular, AI algorithms are capable of automatically identifying morphological structures and performing their morphometric assessment during the analysis of biological tissues and cell cultures. AI tools have high potential for revealing hidden and complex patterns in tabular data from omics studies, enabling the identification of intermolecular interactions and the objective reconstruction of the development of typical pathological processes. The active use of AI code generators to create specialized computer programs eliminates the need for interdisciplinary collaboration in the automation of experimental data processing.

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