Care in Pieces: Unpacking Experiences of Using LLMs for Self-management of Endometriosis and PCOS

Ariane Lucchini, Catalina Lagos Rojas, Alessandro Bozzon, Sara Colombo
In: Proceedings of the 2026 Designing Interactive Systems Conference · 2026 · pp. 1734–1748 · doi:10.1145/3800645.3812813 · W7164506847
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Abstract

Women with endometriosis and Polycystic Ovary Syndrome (PCOS) require individualized, long-term, holistic care, yet systemic healthcare barriers leave many managing independently. Increasingly, women are turning to Large Language Models (LLMs), such as ChatGPT, for support in self-management. However, little is understood about how people use and experience LLMs in this context. We employed a feminist participatory approach to collective knowledge production and situated expertise to explore participants’ motivations and expectations in using LLMs for self-management. We engaged in co-annotation of personal ChatGPT conversations and collage-making sessions with 8 women living with endometriosis and/or PCOS, centering their lived experiences. We surfaced the layered experiences of self-managing endometriosis and/or PCOS with LLMs and how these compare and contrast with their ideal visions of care. We contribute empirical insights and design considerations for LLM systems providing support in this context.

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