Ontology and axiology of artificial intelligence in maternal and neonatal health

Elizabeth Laura Padilla Saavedra

SAP Health and Policy, 2026

Abstract: The integration of artificial intelligence (AI) in maternal-neonatal health transcends technological innovation to become an ontological and axiological reconfiguration of clinical practice. This research analyzes, from a qualitative and philosophical approach, how AI constitutes new clinical realities. Through systematic review, the factum of AI capabilities was established, demonstrating high accuracy (AUC > 0.85; precision > 95%) in predicting preeclampsia, preterm birth, and neonatal mortality. Conceptual analysis based on frameworks by Heidegger (ontology, temporality), Varzi (ontological inventory), and Echeverría (axiology) was employed. Results show AI introduces ontological entities such as 'pre-symptomatic patient' and 'risk score,' altering care's temporal structure toward probabilistic future. AI's 'value' reveals itself as contextual and contested function. Ethical challenges were identified including algorithmic bias, 'black box' opacity, and responsibility gap. It concludes that responsible AI governance demands ontological humility and adaptive regulatory framework prioritizing equity, transparency, and human-centered care.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cwf:shpart:shp2026334

DOI: 10.62486/shp2026334

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