Skip to content
Review Open access

Artificial Intelligence Use in Menopause Care

Jul 2026 · Current Obstetrics and Gynecology Reports · Vol 15 · 0 citations · 36 references

TL;DR

An overview of current applications of artificial intelligence in healthcare with specific focus on uses within women’s health and menopause care is described, aiming to identify which tools and methods have been applied to menopause diagnosis, symptom management, and treatment personalization.

Abstract

This report describes an overview of current applications of artificial intelligence in healthcare with specific focus on uses within women’s health and menopause care. We aim to identify which tools and methods have been applied to menopause diagnosis, symptom management, and treatment personalization, while also reflecting upon the structural and data barriers that prevent equitable use of AI in this domain. As AI has continued to advanced rapidly within healthcare, mature applications such as in drug discovery and diagnostic imaging have shown promise to improve care. Despite this, advancements in menopuase care remain a significant gap: as of 2025, none of the 1,257 FDA-approved AI algorithms focus on menopause diagnosis or treatment. Existing maching learning approaches are limited by data issues such as sparse, inconsistently captured clinical data as well as underrepresentation of large groups of women, partricularly women of color, in research databases. Data missingness disproportionately affects already-underserved populations, meaning application of these AI tools risks amplifying these disparities further. While menopause represents a potential space for development of artificial intelligence solutions, meaningful structural changes to data acquisition and interpretation are required to ensure accurate, impactful, and equitable care.

Read PDF

Similar papers

Review Open access Sep 2026

Artificial Intelligence in Lifestyle Medicine: Advancing Clinical Practice, Education, and Whole-Person Care With Responsible Innovation.

Artificial intelligence (AI) is rapidly transforming health care, offering new opportunities to enhance education, clinical care, and system-level integration. Lifestyle medicine (LM), with its emphasis on behavior change and whole-person care is particularly well positioned to benefit from these advances. This perspec...

L. Lianov · 0 citations
Review Open access Sep 2026

Artificial intelligence in ovarian cancer prevention and control: a brief review of detection, treatment, and equity (2021–2026)

This Mini Review summarizes advances from 2021 to 2026 in artificial intelligence (AI) applications across the OC care continuum, focusing on early detection, treatment decision-making, prognostic assessment, clinical implementation, and health equity.

Zhen-Yu Huang, Bo-Yang Wei, Ying Shen · 0 citations
Review Open access Sep 2026

ARTIFICIAL INTELLIGENCE AND PRECISION MEDICINE IN OBESITY MANAGEMENT: CURRENT APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS

Obesity is a complex, chronic, and heterogeneous disease that affects more than one billion people worldwide and represents one of the greatest public health challenges of the twenty-first century. Despite significant advances in pharmacotherapy, including glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual...

Alicja Włodarczyk, Hubert Łagosz, P. Wites et al. · 0 citations
Review Open access Sep 2026

Artificial intelligence for women’s reproductive health: A scoping review of global diagnostic trends, methodological gaps, and a translational research agenda for low-resource settings

Women’s reproductive and endocrine disorders including Polycystic Ovary Syndrome (PCOS), endometriosis, thyroid disorders, infertility, and pregnancy-related complications remain a major global health burden. These conditions are especially difficult to manage in low- and middle-income countries (LMICs), where diagnost...

Mohammad Mehedi Hasan Munna, Toriqul Islam, Omar Faruk et al. · 0 citations
Open access Aug 2026

Methodology for a Dual-Target Reproductive Intelligence System for Early Prediction of Infertility and Menopause

A dual-target machine-learning reproductive intelligence system for predicting infertility risk and menopause transition from shared health data that integrates clinical records, hormonal profiles, laboratory results, ultrasound findings, menstrual history, age, body mass index, lifestyle factors, infection history and...

Folayemi Faith Adekola, Oyebode Aduragbemi, Olufunke Olubukola Ayennakin et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.