Human Phenotype Ontology (HPO) Mapper: Semantic Mapping of Clinical Findings to the Human Phenotype Ontology Using AI-Powered Embeddings and LLM-Based Quality Control

ABSTRACT Background Structured phenotypic annotations linked to genetic data can drive diagnostic insight and therapeutic discovery in complex diseases. However, poor research access to the rich clinical data trapped in unstructured clinical records remains a significant barrier to phenotype-genotype integration. Here, we present Human Phenotype Ontology (HPO) Mapper, a scalable AI-assisted tool designed to ingest semantically structured clinical findings paired with anatomical region and accurately map them to HPO terms and associated genes. Results We applied HPO Mapper to two forms of standardised clinical input extracted from inflammatory bowel disease (IBD) patient records. The first data type consisted of paired ‘clinical findings + anatomical regions’ derived from unstructured clinical reports and the second was standardised ICD-10 code-derived phenotypes. HPO Mapper achieved high semantic alignment and mapping accuracy for both data types (F1 = 0.85 ± 0.05 and 0.84 ± 0.03, respectively). Additionally, HPO Mapper was able to convert 62.3% of previously unusable free-text entries into HPO terms. Utility was demonstrated at cohort scale, whereby resultant HPO sets projected onto gene space recovered well-established IBD drivers including NOD2 , IL6 , STAT3 , IL10RA , and CTLA4 . Conclusions Our publicly available tool is suitable for converting clinical findings and regions into gene-linked HPO terms for precision medicine. This enables real-time HPO mappings, providing a foundation for scalable AI-driven phenotyping across diseases. More broadly, HPO Mapper provides a generalisable infrastructure for unlocking the latent value of clinical narrative data and bridging the gap between clinical records and genomic diagnostic discovery for targeted therapies. VISUAL ABSTRACT