ARTIFICIAL INTELLIGENCE AND THE DIAGNOSTIC INVISIBILITY OF WOMEN’S METABOLIC DISORDERS: A NARRATIVE REVIEW OF SOCIO-TECHNICAL CHALLENGES IN PCOS/PMOS RECOGNITION - Mehal, Kinga; Pielich, Weronika Martyna; Siusta, Natalia; Gałan, Katarzyna; Kuczyńska, Sandra; Balicka-Dworczak, Natalia (International Journal of Innovative Technologies in Social Science, 2026)
Background: The 2026 proposal to rename Polycystic Ovary Syndrome (PCOS) as Polyendocrine Metabolic Ovarian Syndrome (PMOS) reflects a shift from an ovarian and fertility-centered paradigm toward a systemic endocrine-metabolic understanding of the disorder. Diagnostic delays in PCOS/PMOS are not only clinical but also socio-technical, shaped by fragmented care, gendered interpretation of symptoms, and electronic health record systems that privilege coded reproductive diagnoses over dispersed metabolic, dermatological, and psychological signals. Methods: This narrative review synthesizes literature identified in PubMed and Scopus, focusing primarily on publications from 2015 to 2026 and supplemented by foundational sources on PCOS pathophysiology, clinical guidelines, artificial intelligence, explainable machine learning, electronic health records, and gender bias in healthcare. Results: AI-supported EHR analysis may facilitate earlier recognition of PCOS/PMOS by linking structured laboratory data, ultrasound findings, and unstructured clinical narratives across specialties. Natural language processing may identify early warning signs recorded in dermatology, primary care, gynecology, and endocrinology notes, while predictive machine learning models may detect longitudinal metabolic trajectories before formal diagnosis. However, these systems may reproduce historical bias if trained on incomplete or gendered datasets. Conclusions: AI should be understood as a socio-technical intervention rather than a purely technical diagnostic solution. Its value depends on transparency, explainability, bias-aware validation, and preservation of the patient’s narrative.