Artificial intelligence driven digital reproductive health education: Building a terminology database to improve Spanish health communication training

Liang Xu, Licui Zhu

Abstract

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.

Full Text:

PDF

References

Nutbeam D. The evolving concept of health literacy. Social science & medicine. 2008;67(12):2072-2078.

Kreps GL and Neuhauser L. New directions in eHealth communication: opportunities and challenges. Patient education and counseling. 2010;78(3):329-336.

Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic acids research. 2004;32(suppl_1): D267-D270.

Shahpori R and Doig C. Systematized Nomenclature of Medicine–Clinical Terms direction and its implications on critical care. Journal of critical care. 2010;25(2):364-e1.

Zeng QT, Tse T, Divita G, Keselman A, Crowell J, Browne AC and Ngo L. Term identification methods for consumer health vocabulary development. Journal of medical Internet research. 2007;9(1): e4.

Keselman A, Tse T, Crowell J, Browne A, Ngo L and Zeng Q. Assessing consumer health vocabulary familiarity: an exploratory study. Journal of medical internet research. 2007;9(1): e5.

Campillos-Llanos L. MedLexSp–a medical lexicon for Spanish medical natural language processing. Journal of Biomedical Semantics. 2023;14(1):2.

Cassanello P, Mayer MA and Valero-Alarcón J. Readability of patient health information in Spanish: A systematic review. Journal of Medical Internet Research. 2020;22(3): e13120.

He Z, Chen Z, Oh S, Hou J and Bian J. Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach. Journal of biomedical informatics. 2017; 69:75-85.

Kloehn N, Leroy G, Kauchak D, Gu Y, Colina S, Yuan NP and Revere D. Improving consumer understanding of medical text: development and validation of a new subsimplify algorithm to automatically generate term explanations in English and Spanish. Journal of medical Internet research. 2018;20(8): e10779.

Nadarzynski T, Miles O, Cowie A and Ridge D. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digital health. 2019; 5:2055207619871808.

Latt PM, Aung ET, Htaik K, Soe NN, Lee D, King AJ and Fairley CK. Evaluation of artificial intelligence (AI) chatbots for providing sexual health information: a consensus study using real-world clinical queries. BMC Public Health. 2025;25(1):1788.

Fetrati H, Chan G and Orji R. Leveraging Generative and Rule-Based Models for Persuasive STI Education: A Multi-Chatbot Mobile Application. In: Proceedings of the 7th ACM Conference on Conversational User Interfaces; 2025. p. 1-9.

Dahò M and Caci B. Exploring AI-assisted design of executive function rehabilitation programs for individuals with ADHD: a mixed-methods evaluation of prompts and chatgpt outputs. BMC psychology. 2025.

Ayre J, Bonner C, Muscat DM, Dunn AG, Harrison E, Dalmazzo J and McCaffery KJ. Multiple automated health literacy assessments of written health information: development of the SHeLL (Sydney Health Literacy Lab) Health Literacy Editor v1. JMIR Formative Research. 2023;7(1):e40645.

Denecke K, Gabarron E, Grainger R, Konstantinidis ST, Lau A, Rivera-Romero O and Merolli M. Artificial intelligence for participatory health: applications, impact, and future implications. Yearbook of medical informatics. 2019;28(01):165-173.

Santos MR and Carvalho LC. AI-driven participatory environmental management: Innovations, applications, and future prospects. Journal of Environmental Management. 2025; 373:123864.

Imundo MN, Watanabe M, Potter AH, Gong J, Arner T and McNamara DS. Expert thinking with generative chatbots. Journal of Applied Research in Memory and Cognition. 2024.

Roth CB, Papassotiropoulos A, Brühl AB, Lang UE and Huber CG. Psychiatry in the digital age: A blessing or a curse? International journal of environmental research and public health. 2021;18(16):8302.

Yigzaw KY, Olabarriaga SD, Michalas A, Marco-Ruiz L, Hillen C, Verginadis Y and Chomutare T. Health data security and privacy: Challenges and solutions for the future. In: Roadmap to successful digital health ecosystems; 2022. p. 335-362.

Bai G, Pan X, Zhao T, Chen X, Liu G and Fu W. Quality assessment of YouTube videos as an information source for testicular torsion. Frontiers in public health. 2022; 10:905609.

Refbacks

  • There are currently no refbacks.