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

Background: Gender 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. Methods: We 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. Results: We 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. Conclusions: While 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.