Artificial intelligence driven digital reproductive health education: Building a terminology database to improve...

This study proposes an artificial intelligence based pipeline to create a standardized Spanish reproductive health terminology database, addressing challenges like inconsistent terminology and direct machine translation in existing digital education resources. The methodology involves: (1) corpus acquisition of educational materials and user queries, (2) automatic term extraction and normalization, (3) mapping to established biomedical terminologies (e.g., UMLS, SNOMED CT), and (4) generating simplified definitions and examples to bridge clinical and consumer language gaps. Prior research on Spanish medical vocabulary and text simplification supports this approach. The evaluation plan includes expert validation (clinicians/educators), intrinsic term quality control (coverage, ambiguity, synonymy), and learner-focused assessments (term recognition and comprehension). The expected outcome is a reusable terminology tool and a replicable framework that enhances Spanish health communication capacity in reproductive health education without compromising interoperability or digital education processes.