Reimagining biomedical science workflows in the age of large language models
Abstract Large language models (LLMs) are generative artificial intelligence (AI) models that are rapidly reshaping the practice of biomedical science. Their ability to synthesize literature, generate analytical code, and interface with multimodal data offers a new framework for accelerating discovery. Yet their integration into scientific workflows remains irregular, and the field lacks clear guidance for reliable and productive use. We review emerging evidence on researcher adoption, highlight common failure modes such as so-called hallucinations (i.e. confabulations) and overgeneralization, and provide practical recommendations for domain-informed use of LLMs in basic biomedical research. We structure this review around four domains in which LLMs increasingly augment biomedical science: administrative tasks, literature search and synthesis, data analysis, and scientific writing. For each domain we provide practical guidance, illustrative use cases, and examples of free or low-cost tools that researchers can readily adopt. Finally, we discuss the organizational and cultural changes for biomedical science to leverage LLMs responsibly, including transparent reporting, human-in-the-loop validation, and alignment with scientific rigor and reproducibility standards. Together, these recommendations provide a path for integrating LLMs into biomedical research in ways that enhance, rather than replace, human expertise and accelerate the path from biological insights to beneficial human impact.