Using AI to Manage Menstrual Symptoms for Sustainable Development

Abstract

Artificial intelligence has started transforming healthcare into one of those approaches to managing menstrual health using predictive analytics, personalized interventions, and sustainable solutions. This paper synthesizes the results obtained from 30 different peer-reviewed journals that have researched AI applications in menstrual health, such as machine learning algorithms for symptom tracking, natural language processing for user engagement, and real-time monitoring through integration of wearable technology. It has categorized AI applications into three main categories with details such as the first symptom prediction category consisting of AI models which forecast cycle irregularities and symptoms that point towards the health risks such as polycystic ovary syndrome and endometriosis. Personalized care using AI-based recommendations for lifestyle changes, methods of contraception, and hormonal balance is another classification, with sustainable benefits arising from AI application in providing resource-efficient solutions in menstrual health by optimizing telehealth consultations and digital self-care platforms. The findings are also reported in a nutshell in a comprehensive tabular and graphical format to show how AI improves diagnostic accuracy, lowers healthcare costs through early intervention, and drives gender inclusivity in digital health innovations. In fact, the AI-powered menstrual health intervention falls under the Sustainable Development Goals (SDGs) in such a way that it is about gender equality (SDG 5) through inclusive digital health tools; good health and well-being (SDG 3) through advancing reproductive healthcare; and innovation in health care (SDG 9) from AI-based research. It delineates challenges like algorithmic bias, data privacy, and the limited reach of services into low-resource settings, thus necessitating ethical AI frameworks, along with including data collection. This paper indicates that AI can be transformative for menstrual health management and advance the field for more equitable, affordable, and data-driven reproductive health solutions.