A text mining and ontology-based approach using phenotypes to obtain relevant literature for rare diseases.

Diagnosing rare diseases remains a major challenge due to limited clinical knowledge and the frequent absence of diagnostic criteria. We present a digital framework that leverages large language models and biomedical text embeddings to bridge this gap. By mapping Human Phenotype Ontology terms to a shared vector space with millions of PubMed abstracts and full-text articles, our method enables phenotype-driven semantic search and ranks literature relevant to patient symptoms, even without explicit disease mentions. Validated on OMIM-derived benchmarks and applied to RASopathies, including NF1, Noonan, and Costello syndromes, our approach retrieved expected findings, supporting differential diagnosis and research. The framework is implemented in an open-source Python package, py-semtools, and it can be integrated into clinical decision support systems or adapted to other ontologies and corpora. This work demonstrates how AI-driven informatics can enhance rare disease diagnosis and exemplifies the role of digital tools in transforming precision medicine and healthcare delivery.