AI-enhanced exploration of depression among women during menopause: Implications for health education

Depression affects millions of women worldwide, particularly during menopausal transitions. Artificial intelligence (AI) technologies now offer innovative approaches to the awareness, early identification, and educational management of depression, complementing conventional healthcare interventions. The authors explored depression across menopausal stages; premenopause, perimenopause, and postmenopause while examining how AI-enhanced tools, including chatbots and machine learning algorithms, can improve health education outcomes for women experiencing perimenopausal depression. A narrative review synthesised literature from PubMed and related databases covering depression epidemiology during menopause and AI applications in women's mental health education. Studies from 2020 to 2025 were reviewed, with particular attention to recent longitudinal evidence, systematic reviews, and AI intervention trials. Depression was assessed using validated screening instruments, including the Patient Health Questionnaire-9 (PHQ-9) and the Centre for Epidemiologic Studies Depression Scale (CES-D). Depression prevalence increases significantly during perimenopause compared to premenopause, declining after postmenopause. Risk factors include hormonal fluctuations, prior depression, stress, sleep disturbance, and poor social support. AI interventions show promise: cognitive behavioural therapy-based chatbots significantly reduce depression symptoms, while machine learning algorithms achieve high accuracy in identifying depression-indicative patterns. These tools hold considerable potential for health education, awareness creation, and early referral support. Integrating AI-powered tools with conventional health education offers scalable, accessible support for perimenopausal women, particularly those facing barriers to conventional healthcare services.