Artificial Intelligence-Based Thematic Analysis of Biomedical Literature for Precision Nutrition

The scale of contemporary biomedical research challenges the ability of traditional reviews to preserve structural awareness across entire fields. This limitation is particularly consequential in precision nutrition, where feature selection and model interpretability depend on understanding how diverse and evolving data streams intersect to influence metabolic response. However, as literature volume grows, visibility across the full evidence landscape diminishes. To address this gap, we developed an end-to-end computational framework that ingests complete PubMed query results and organizes all returned abstracts into unsupervised thematic structures. Structured queries were programmatically executed, and abstracts were analyzed using complementary natural language processing approaches. To determine whether traditional reviews reflect the thematic distribution of primary research, review and nonreview subsets were clustered independently and compared using cluster-centroid cosine similarity heat maps. To demonstrate scalability under realistic precision nutrition integration demands, the framework mapped >385,000 abstracts spanning genetics and physical activity into stable thematic representations. Although clinically consolidated domains showed strong correspondence between review and primary research clusters, several mechanistic and genomics-focused areas demonstrated comparatively limited representation in review-derived structures. The full pipeline was operationalized in an interactive web-based application (https://medreview.streamlit.app/), enabling reproducible corpus-scale structural mapping and review-gap diagnostics without manual query navigation or programming. By transforming literature synthesis into scalable structural analysis, this framework provides a quantitative infrastructure for evidence-grounded modeling in precision nutrition research.