Artificial Intelligence in Menopause Management

¹Mexican Academy of Surgery, Mexico

²National Academy of Medicine of Mexico, Mexico

³women's Health Clinic, Mexico

*Corresponding author: Victor Manuel Vargas-Hernandez, Mexican Academy of Surgery, National Academy of Medicine of Mexico & Women's Health Clinic, Mexico, Phone: (52)5552 179782, E-mail: [email protected]

Received Date: March 28, 2025

Published Date: April 26, 2025

Citation: Vargas-Hernandez VM, et al. (2025). Artificial Intelligence in Menopause Management. Mathews J Gynecol Obstet. 9(1):45.

Copyrights: Vargas-Hernandez VM, et al. © (2025).

ABSTRACT

Menopause, a natural stage in a woman's life, is accompanied by various physical and emotional symptoms. Technological advances, such as artificial intelligence (AI), are revolutionizing women's health with effective treatment options and not only extending human intelligence to personalized management, offering physicians promising solutions to the complexities of menopause, transforming treatment through personalized symptom tracking and management. AI-powered apps and devices allow women to monitor and analyze their symptoms in real time, collecting data on symptom frequency and intensity. Their algorithms provide personalized recommendations for lifestyle changes, dietary adjustments, and appropriate treatment options. AI can provide accessible support to women navigating this menopausal transition.

Keywords: Vasomotor Symptoms, Menopause, Comorbidities, Menopausal Hormone Therapy.

BACKGROUND

Artificial intelligence (AI) not only mimics but also greatly extends human intelligence. Machine learning and deep learning models based on artificial neural networks draw on diverse data, including clinical images and medical notes, as well as genomic and sensor-generated data. These models can iteratively learn from clinical databases and bring the expertise of multiple medical specialties to bear on individual data. Medical decisions and personalized therapy for a single patient are based on collective experience. Their menopause is individualized. Currently, artificial intelligence (AI) offers promising solutions for physicians in the complexities of menopause, with both benefits and challenges [1,2]. The menopause transition (MT) brings with it a multitude of changes, both physiological and psychological, due to ovarian senescence. Menopause is a biomarker that not only indicates a loss of fertility, but also increases the risk of various problems and diseases of midlife (40 to 65 years), such as cardiovascular disease (CVD), endometrial cancer (EC), mental health problems, etc.; A critical marker of reproductive aging, the age at which menopause begins is a sign of somatic aging and general health. There is a link between early menopause and shorter life expectancy, bone mineral density (BMD), an increased risk of fractures, all-cause mortality, CVD and cardiovascular death, and cancer.

AI-powered virtual assistants and chatbot software serve as invaluable resources for women, providing personalized support and information related to menopausal symptoms. These AI entities help women make informed decisions about treatment options and lifestyle modifications. AI analyzes vast amounts of data from individual health records, wearable devices, and self-reported symptoms to provide personalized recommendations for managing specific menopausal symptoms. An AI-powered system tracks a woman's sleep patterns, mood fluctuations, and vasomotor symptom (VMS) frequency to suggest tailored interventions, such as lifestyle changes, dietary adjustments, or menopausal hormone therapy (MHT). Natural language processing is used to offer personalized advice on symptom management, MHT, and alternative treatments, all based on individual preferences and medical history [3]. Health Monitoring and Early Detection AI algorithms can monitor changes in women's health parameters over time and detect subtle signs of health risks associated with menopause, such as loss of bone mineral density (BMD) or cardiovascular problems. By identifying these risks early, doctors can proactively intervene to prevent or mitigate potential complications.

Precision Medicine with AI

AI-powered precision medicine approaches help tailor menopause treatments to individual genetic, hormonal, and lifestyle factors, optimizing efficacy and minimizing side effects. By analyzing large-scale genomic and clinical data, AI algorithms identify biomarkers associated with menopause symptoms and response to treatments, enabling clinicians to provide personalized and effective management.

Benefits of AI for Patients and Clinicians

In personalized care, AI facilitates the development of customized care pathways tailored to each patient's distinct needs, preferences, and risk factors (RFs), improving treatment adherence and outcomes. in resource efficiency and decision support systems driven helps to prioritize interventions and allocate resources efficiently, thus optimizing the delivery of patient care [4], also improves knowledge with educational tools driven and decision support systems in patients and doctors with up-to-date information on evidence-based recommendations, encouraging shared decision-making, improving medical competence, in research it accelerates it by analyzing large data sets, identifying patterns and generating hypotheses for further research [4].

Application of AI in the Clinic

The success of AI tools in clinical practice integration requires interdisciplinary collaboration, clinical training, and infrastructure support to ensure seamless workflow integration and user acceptance; however, long-term efficacy and safety require further research to evaluate the long-term efficacy, safety, and cost-effectiveness of AI-powered interventions in menopause management on a large scale for real-world implementation [5,6].

Artificial intelligence in menopause management

AI improves the accuracy of menopause diagnosis. With the help of machine learning algorithms, it analyzes medical history, symptoms, and hormone levels to determine if you are experiencing menopause or other related conditions, reducing misdiagnoses and enabling timely and targeted interventions. For risk prediction and prevention, AI algorithms are able to examine large data sets to identify women at higher risk of experiencing menopause-related complications, such as osteoporosis (OP), cardiovascular disease (CVD), and cognitive decline. Using clinical, genetic, and lifestyle data, AI models provide personalized risk assessments and preventative strategies. The use of machine learning algorithms to predict the risk of osteoporotic fractures in postmenopausal women enables early interventions to mitigate risks to bone health, helps researchers better understand the underlying mechanisms of menopausal symptoms, develop novel treatments, and identify factors that influence individual variations in symptom severity and treatment response, such as behavioral and mental health support that have a significant impact on this, causing symptoms such as anxiety, depression, and cognitive changes. AI-powered mental health apps can provide women with evidence-based interventions, such as cognitive behavioral therapy or mindfulness exercises, to help them cope with the emotional challenges associated with menopause and improve their overall well-being. AI-enabled wearables continuously monitor physiological parameters with continuous feedback: such as heart rate variability, skin temperature, and activity levels, providing real-time information to women about their health status and potential triggers of menopausal symptoms. Continuous monitoring enables early intervention and adjustment of management strategies based on individual responses [7,8].

Optimizing Menopause Management

AI algorithms are capable of examining treatment response data in diverse patient populations, thereby optimizing MHT regimens and minimizing adverse effects. Through continuous monitoring and feedback mechanisms, AI-driven systems adapt treatment plans in real time, ensuring optimal outcomes. AI algorithms are used to personalize hormone replacement therapy doses based on individual symptoms, genetic factors, and treatment responses, improving efficacy and patient satisfaction. Application to vasomotor symptoms (VSS) improved the performance of sternal skin conductance in detecting hot flashes with a sensitivity of 81% and a specificity of 97%. The use of artificial neural networks for endometrial cancer screening in postmenopausal women achieved a sensitivity of 86% and a specificity of 83% [1-3,9]. By bringing together clinical, genetic, and lifestyle data, AI can also improve personalized treatment for menopause, combining a clustering algorithm with knowledge-based algorithms to recommend menopausal hormone therapy (MHT) [2,3,10].

The limited use of AI in the study of cardiovascular risk factors (RF) in menopausal women that affect long-term health, survival, and quality of life (QOL), and the analysis of clinical data to evaluate and revise the standard definition of cardiovascular problems by identifying biomarkers that independently predict long-term mortality [1-3,11].

Subjective cognitive impairment (SCID) refers to the perceived decline in a person's memory or other cognitive functions. One of the most common symptoms associated with MD is particularly concerning, indicating an increased risk of serious neurodegenerative diseases, such as Alzheimer's disease. Machine learning models can more rapidly identify severe SCI during the menopause transition, opening the door to better management of cognitive health: risk factors (RFs), including aging, systemic arterial hypertension (SAH), obesity, and depression, among others. A challenge is that most current cognitive health models focus on dementia, an incurable disease that offers limited opportunities for clinical intervention. Although SCI does not always predict long-term cognitive changes or dementia, a predictive model for cognitive decline and related factors could allow for early intervention to protect cognitive health. A machine learning model identifies those experiencing severe SCI, along with associated RFs, serving as a novel guide for preserving cognitive health in MD. Early identification of high-risk individuals may allow for targeted interventions to protect cognitive health [3,12].

Although there has been progress in the application of AI in the study of women's health during and after the menopausal transition (MT), there is still no evidence of its application in clinical practice. The identification and measurement of underlying mechanisms, along with access to larger datasets within an ethical framework that guarantees privacy and data protection, are needed for the application of deep learning models, which allow for the extraction of features and patterns that expose underlying and relevant characteristics through training with large amounts of data. It is important to understand that AI algorithms can assist and facilitate the work of physicians and enhance them, not replace them. These limitations must be carefully studied and understood to ensure that the promise of AI is appropriately developed and utilized to assist with routine care and precision care, as well as personalized prevention and treatment. It also plays an important role in the development of drugs that could alleviate specific menopausal symptoms. AI has revolutionized the field of women's health, offering new possibilities for empowerment and improved health outcomes, including access to personalized recommendations and support tailored specifically to their needs. One of the key ethical considerations is data privacy and security. Since AI relies heavily on data collection and analysis, it is necessary to ensure that sensitive information related to women's health is protected and used responsibly. Safeguarding patient confidentiality and preventing unauthorized access to personal health records is paramount. Potential risks associated with AI in women's health include overreliance on the technology and the possibility of misdiagnoses or missed diagnoses. It is important to remember that AI is a tool that should augment, rather than replace, healthcare professionals [3,12].

Future Challenges of AI in Menopause

The integration of AI into menopause management raises concerns about data privacy, security, and algorithmic bias, highlighting the need for strong regulatory and ethical guidelines regarding data privacy and security. AI also plays an important role in drug development, helping researchers identify potential compounds that could alleviate specific menopausal symptoms. It is crucial to strike a balance between integrating AI and preserving human expertise and compassion [1-3,13].

CONCLUSION

AI holds great promise for revolutionizing menopause treatment by providing personalized, efficient, and evidence-based solutions for both patients and healthcare professionals. By harnessing the power of AI, we can elevate the quality of care, optimize treatment outcomes, and empower women to navigate the menopause transition with confidence and resilience.

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