55 Research intelligence for women’s health: Preliminary results from an Ask-Your-Data Utility powered by large language models

Objectives/Goals: To contextualize existing knowledge in translational science, women’s health research can benefit from artificial intelligence (AI) methods for obtaining insights out of massive scientific literature. This study explored the feasibility of making an AI model answer diverse questions based on a set of our institution-specific publications. Methods/Study Population: We developed an automated pipeline to retrieve PubMed publications on women’s health that acknowledged the Mayo Clinic Clinical and Translational Science Award of the past two cycles. The publications need to be indexed with MeSH terms (as a major topic) including “Women’s Health”, “Obstetrics”, “Gynecology”, “Female Urogenital Diseases and Pregnancy Complications”, and “Perinatal Care”, etc. In addition, we enforced the species to be “Human” and the demographics to include “Female.” The metadata of every article including title, abstract, publication year, journal, and author affiliation were saved into a single JSON file. The JSON file was then used as the input context along with a few test prompts submitted to the gemini-2.5-flash model. All the experiments were conducted in a Google Colab environment. Results/Anticipated Results: A total of 237 articles on women’s health were retrieved for the experiment. The AI model correctly summarized the top 5 journals but did not obtain the correct article counts until explicitly requested to heed accuracy. When asked to list three prominent departments and their focus areas, the model gave sensible answers aligned with our knowledge. When asked to identify 10 articles that best represent the T4 stage, the model returned appropriate answers that demonstrated an understanding of the implied emphasis on population health and widespread implementation. Lastly, the model provided well-organized advice with annotated experts and strategies, when asked to recommend collaborators for doing AI research in women’s health. Discussion/Significance of Impact: Based on a literature dataset, the general-purpose AI model delivered impressive results when asked to fulfill tasks that involved information extraction, summarization, reference to external knowledge, and strategy consultation. The ongoing work aims to enhance scalability and accessibility of the tool while adding more rigorous evaluations.