The emergence of artificial intelligence (AI) has had a significant impact on medical research and practice, both in terms of the number of studies and research paradigms, and has become an important tool for the development of pharmacoepidemiology. However, traditional AI has faced many challenges, while facilitating pharmacoepidemiology research, such as complex data processing, difficulty in identifying drug exposures and potential outcomes, and time-consuming and laborious study design and implementation. The rapid development of generative AI, represented by large language models (LLMs), has demonstrated a unique potential to enhance research efficiency, shift research paradigms, and facilitate knowledge discovery. LLMs are equipped with natural language understanding and generation capabilities. Through deep mining of multi-dimensional data resources, LLMs can quickly and accurately extract, analyze, summarize, and present the required information, which can not only help drug discovery, drug repurposing, pharmacovigilance and other pharmacoepidemiological tasks, but also provide powerful support for the whole process of research protocol design, data analysis, result interpretation and paper publication. Driven by LLMs, pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis. Of course, LLMs also have problems of data bias, “illusion” of results, and ethical and legal regulation. By strengthening interdisciplinary cooperation, establishing a standardized evaluation system, improving ethical and regulatory guidance, enhancing data quality, strengthening practitioner training and capacity building, and promoting human-machine collaborative research modes, it is expected that the potential of LLMs in pharmacoepidemiology will be fully released, and it will provide a more scientific, rapid, and efficient technological support for drug regulation and public health decision-making.

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