Large Language Model-Based Evaluation of the Impact of Gender in Medical Research

BackgroundGender disparities in academic medicine have been previously reported, but prior bibliometric studies have been limited by small sample sizes and reliance on manual gender annotation methods. These bottlenecks constrain previous analyses to only a small subset of clinical literature. To assess gender-based differences in authorship trends, research impact, and scholarly output over time in clinical research at scale, we hypothesized that large language models (LLMs) can be an effective tool to facilitate systematic bibliometric analysis of academic research trends. MethodsWe conducted a retrospective, cross-sectional bibliometric study evaluating manuscripts published between January 2015 and September 2025 across over 1,000 PubMed-indexed academic medical journals. Over 1 million manuscripts, written by more than 10 million authors across 13 medical specialties, were analyzed. To enable this large-scale study, the genders of manuscript authors were annotated using a scalable LLM-based pipeline compatible with consumer-grade hardware. ResultsWe found that the proportion of female principal investigators has increased over time across different medical subspecialties. However, studies led by male authors tended to be published in higher-impact journals and cited more frequently than those led by female authors. We also observed that researchers of the same gender tended to work together when compared to colleagues of the opposite gender. ConclusionsWhile our findings revealed persistent gender-based differences in authorship trends, citation practices, and journal placement, we also observed ongoing, meaningful progress in female representation within academic medical research over time. Our results suggest that LLMs can be a powerful tool to scalably and periodically track this continued progress in future academic medical research. Plain Language SummaryAcademic research is important to advance the field and practice of medicine. To obtain an accurate picture of the differences in medical research and impact between male and female researchers, we leveraged large language models (LLMs) to identify author genders for over one million medical research papers published between 2015 and 2025. We found that the number of women serving as lead researchers has increased over time across many medical specialties. However, important gaps in achieving gender equality in medical research remain. Our study ultimately helps demonstrate that LLMs can help us monitor gender-based trends in academic research in the future.