ARTIFICIAL INTELLIGENCE AND DIGITAL METHODS IN BIOLOGICAL RESEARCH
DOI:
https://doi.org/10.5281/Keywords:
artificial intelligence, machine learning, deep learning, bioinformatics, genomics, , digital microscopy, biodiversity monitoring, laboratory automationAbstract
High-throughput sequencing, single-cell omics, digital microscopy, remote sensing, and automated sensors have dramatically increased the volume and complexity of data generated in biological research. This literature review aims to systematize the main applications of artificial intelligence and digital methods in biological research, their scientific potential, and the conditions required for their reliable use. A thematic synthesis of peer-reviewed studies demonstrates that artificial intelligence delivers significant results in genomic variant detection, cell segmentation, protein structure prediction, single-cell data integration, screening of novel molecules, biodiversity monitoring, and laboratory workflow automation. However, high predictive performance does not establish that a biological mechanism has been proven: data leakage, sampling bias, domain shifts, limited interpretability, and the lack of transparency of code and models hinder the generalizability of results. The literature synthesis indicates that artificial intelligence should be regarded not as a tool for replacing biologists, but as a research infrastructure governed by data quality control, external validation, domain expertise, and reproducibility standards.
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