# Artificial Intelligence in Postmenopausal Health: From Risk Prediction to Holistic Care

## TL;DR

This paper reviews how AI can improve postmenopausal health by predicting risks, enabling early detection, and supporting personalized care.

## Contribution

The paper highlights novel AI applications in postmenopausal health, including real-time symptom monitoring and reducing healthcare disparities.

## Key findings

- AI improves accuracy in breast cancer and osteoporosis screening through imaging analysis.
- Wearable devices and NLP help monitor underreported symptoms like hot flushes and mood disorders.
- Digital tools support personalized interventions and reduce healthcare disparities via telemedicine.

## Abstract

Background/Objectives: Menopause, marked by permanent cessation of menstruation, is a universal transition associated with vasomotor, genitourinary, psychological, and metabolic changes. These conditions significantly affect health-related quality of life (HRQoL) and increase the risk of chronic diseases. Despite their impact, timely diagnosis and individualized management are often limited by delayed care, fragmented health systems, and cultural barriers. Methods: This review summarizes current applications of artificial intelligence (AI) in postmenopausal health, focusing on risk prediction, early detection, and personalized treatment. Evidence was compiled from studies using biomarkers, imaging, wearable sensors, electronic health records, natural language processing, and digital health platforms. Results: AI enhances disease prediction and diagnosis, including improved accuracy in breast cancer and osteoporosis screening through imaging analysis, and cardiovascular risk stratification via machine learning models. Wearable devices and natural language processing enable real-time monitoring of underreported symptoms such as hot flushes and mood disorders. Digital technologies further support individualized interventions, including lifestyle modification and optimized medication regimens. By improving access to telemedicine and reducing bias, AI also has the potential to narrow healthcare disparities. Conclusions: AI can transform postmenopausal care from reactive to proactive, offering personalized strategies that improve outcomes and quality of life. However, challenges remain, including algorithmic bias, data privacy, and clinical implementation. Ethical frameworks and interdisciplinary collaboration among clinicians, data scientists, and policymakers are essential for safe and equitable adoption.