Large language models (LLMs) have emerged as a game-changer in computational tools and are expected to be used in hypothesis generation, drug discovery, literature analysis, medical question answering, and automated data processing. This systematic review aims to gather and combine extensive evidence on the use of LLMs in scientific research, together with the challenges encountered, according to the PRISMA 2020 guidelines. Systematic searches were undertaken in major academic databases from 2019–2025 for peer-reviewed studies and preprints. LLMs are applied to autonomous chemical research, multi-agent hypothesis generation systems, medical QA systems at expert level, and retrieval-augmented generation systems for knowledge synthesis. Major issues remain, including hallucinations and factual inaccuracy, reproducibility and methodological validation, privacy concerns and data security, and algorithmic bias. While LLMs can be highly useful and perform at an expert level in some scientific domains when properly validated and combined with domain knowledge, their deployment requires rigorous validation and field-specific fine-tuning. This review shows that strategic application of LLMs, particularly through self-reflection, knowledge graph integration, and retrieval augmentation, can significantly accelerate scientific discovery without compromising research integrity, while interdisciplinary cooperation is essential to develop governance frameworks, benchmarks, and ethical principles for sustainable scientific use.
- Citation du texte
- Anonymous (Auteur), 2026, Large Language Models in Scientific Research. A Systematic Review of Applications and Challenges, Munich, GRIN Verlag, https://www.grin.com/document/1759642
