{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:17:28Z","timestamp":1791893848154,"version":"4.3.4"},"reference-count":119,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Digital Engineering and Artificial Intelligence Laboratory (DEAL), Department of Critical Care Medicine, Mayo Clinic, Florida, Jacksonville, USA"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jcm14217651","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T03:44:39Z","timestamp":1761795879000},"page":"7651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Artificial Intelligence in Postmenopausal Health: From Risk Prediction to Holistic Care"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6631-2988","authenticated-orcid":false,"given":"Gianeshwaree Alias Rachna","family":"Panjwani","sequence":"first","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3354-7317","authenticated-orcid":false,"given":"Srivarshini","family":"Maddukuri","sequence":"additional","affiliation":[{"name":"Roxborough Memorial Hospital, Philadelphia, PA 19128, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rabiah Aslam","family":"Ansari","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9258-3159","authenticated-orcid":false,"given":"Samiksha","family":"Jain","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manisha","family":"Chavan","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naga Sai Akhil Reddy","family":"Gogula","sequence":"additional","affiliation":[{"name":"Duke Molecular Physiology Institute, Duke University, NC 27708, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gayathri","family":"Yerrapragada","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9628-7090","authenticated-orcid":false,"given":"Poonguzhali","family":"Elangovan","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2430-7370","authenticated-orcid":false,"given":"Mohammed Naveed","family":"Shariff","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6640-7942","authenticated-orcid":false,"given":"Thangeswaran","family":"Natarajan","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayarajasekaran","family":"Janarthanan","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiva Sankari","family":"Karrupiah","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keerthy","family":"Gopalakrishnan","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Wright Medical Center, Scranton, PA 18503, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Divyanshi","family":"Sood","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, UCHealth Parkview Medical Center, Pueblo, CO 81003, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3251-5415","authenticated-orcid":false,"given":"Shivaram P.","family":"Arunachalam","sequence":"additional","affiliation":[{"name":"Digital Engineering & Artificial Intelligence Laboratory (DEAL), Mayo Clinic, Jacksonville, FL 32224, USA"},{"name":"Division of Pulmonology, Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/S0140-6736(98)05352-5","article-title":"The menopause","volume":"353","author":"Greendale","year":"1999","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0378-5122(95)00985-X","article-title":"The normal menopause transition: An overview","volume":"23","author":"Mckinlay","year":"1996","journal-title":"Maturitas"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0378-5122(95)00968-X","article-title":"The demography of 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