AI-Assisted Digital Health Framework for PCOS/PCOD Management

Polycystic Ovary Syndrome/Polycystic Ovary Disease (PCOS/PCOD) continues to pose persistent clinical and social challenges, not solely due to its endocrine complexity, but because of long-term discontinuity in lifestyle-based care. While lifestyle modification is widely recommended as a first-line management strategy, real-world implementation often remains fragmented, episodic, and weakly monitored, leading to poor adherence and variable outcomes. This paper advances a continuity-focused digital health perspective for PCOS/PCOD management by conceptualizing a data-assisted framework that supports sustained lifestyle engagement rather than isolated clinical interventions. The proposed approach integrates patient-reported inputs, behavioural indicators, and routinely observable physiological signals within a unified digital ecosystem. Artificial intelligence is positioned as a supportive analytical layer that enables pattern recognition, adaptive feedback, and longitudinal insight, while preserving clinician oversight and patient agency. Unlike application-centric or automation-driven solutions, the framework emphasizes care continuity, interpretability, and ethical responsibility, addressing practical constraints related to accessibility, data trust, and personalization. The paper does not claim clinical prediction or automated diagnosis; instead, it contributes a structured design rationale for embedding AI-assisted monitoring into long-term PCOS/PCOD care pathways. By reframing PCOS/PCOD management as an ongoing, data-informed care process rather than a series of isolated recommendations, this study offers a scalable and patient-centric conceptual foundation for future digital health interventions targeting chronic lifestyle-sensitive conditions.