This article comprehensively analyzes the role of artificial intelligence (AI) technologies in scientific research. Using systematic review methods, the effectiveness of machine learning (ML) and deep learning (DL) methods in scientific processes, the advantages and limitations of using generative AI tools (ChatGPT, Gemini, Scispace, Perplexity) are discussed. The results show that AI technologies provide significant advances in working with big data, advancing scientific hypotheses, and automating laboratory processes. However, issues such as algorithmic bias, the "black box" problem, hallucinations, plagiarism, confidentiality, and copyright continue to concern the scientific community. In particular, this article puts forward recommendations for the full and rational use of AI, ensuring transparency and fairness in scientific processes, and operating within the rules of scientific ethics.
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