{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T12:08:39Z","timestamp":1771848519162,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686066","type":"print"},{"value":"9781643686073","type":"electronic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,3]]},"abstract":"<jats:p>Background: Menopause, endometriosis, miscarriage, and female infertility are health issues affecting women worldwide (nearly half the global population). The biomedical literature is human-readable and evergrowing with around 3.5K papers published daily. Current Artificial Intelligence (AI) cannot reliably deal with facts, and automatic processing of scientific publications to obtain reliable insights remains a challenge, although, representing diseases in an actionable (machine-interpretable semantics) has a long-standing tradition in biomedical research. Objective: Conduct some experiments to explore to what extent ontologies and knowledge graphs (symbolic AI) can support comprehensive human-centric explainable AI for artificial neural networks (neural AI). Methods: Instead of using Large Language Models (LLMs) from neural AI as all-in-one generative AI solution, this paper investigates a neuro-symbolic AI approach, combining neural AI (to process and extract patterns for health issues from free-text) with symbolic AI (explicit representations of background knowledge). Our neuro-symbolic AI approach leverages on domain knowledge (simple classification based on predefined categories), and scientific evidence from the biomedical literature to provide human-readable explanations (explainable AI) formally represented as nanopublications (machine-processable knowledge graphs). We investigated if incorporating prior domain knowledge (best scientific evidence) into vector arithmetic formulas may support customisation (e.g. bringing \u201cunseeing\u201d terms for a predefined category). Results: We performed three experiments (EXP1, EXP2 and EXP3), evaluating 315 candidate n-grams obtained by applying unsupervised vector arithmetic formulas (cosine for similarity and 3CosAdd for four-term analogies) to word2vec embeddings created from 301,201 PubMed citations (titles and abstracts). We also conducted a fourth experiment (EXP4) with 9 LLMs to automatically extract and classify terms from evidence-based text excerpts, evaluating 381 terms from LLMs\u2019 output. In EXP4, we looked into the output categories provided by 2 open-source small-size biomedical LLMs (with 66.4 and 184 millions of trainable parameters) and 7 free-of-charge general LLMs (DeepSeek-V3, Groq, Grok3-beta, QWEN2.5-MAX, Gemini, Claude, ChatGPT4). Conclusion: Biomedical knowledge may guide explainability (what predictions from neural models are worthy to explain and what predictions can be ignored) and enable a higher level of customisation when using word2vec embeddings and LLMs for extracting patterns for health issues from free-text.<\/jats:p>","DOI":"10.3233\/shti251460","type":"book-chapter","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T11:35:03Z","timestamp":1771846503000},"source":"Crossref","is-referenced-by-count":0,"title":["Neuro-Symbolic AI for Women\u2019s Health"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9469-2068","authenticated-orcid":false,"given":"Mercedes","family":"Arguello","sequence":"first","affiliation":[{"name":"BCS SGAI, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julio","family":"Des Diz","sequence":"additional","affiliation":[{"name":"Hospital do Saln\u00e9s, Villagarc\u00eda de Arousa, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Jukes","sequence":"additional","affiliation":[{"name":"BCS SGAI, UK"},{"name":"University of Hertfordshire, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Jesus","family":"Fernandez Prieto","sequence":"additional","affiliation":[{"name":"Salford Languages, University of Salford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chloe","family":"Henson","sequence":"additional","affiliation":[{"name":"Gold Coast University Hospital, Queensland Health, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saihong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Stirling, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Furmston","sequence":"additional","affiliation":[{"name":"University of Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Keane","sequence":"additional","affiliation":[{"name":"University of Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Handbook on Smart Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251460","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T11:35:03Z","timestamp":1771846503000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251460"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,3]]},"ISBN":["9781643686066","9781643686073"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251460","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,3]]}}}