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AI’s Role in Advancing Women’s Mental Health Explored – Archives of Women’s Mental Health

Archives of Women's Mental Health Social Media Editorial Team/LinkedIn

Dec 22, 2025, 16:35

AI’s Role in Advancing Women’s Mental Health Explored – Archives of Women’s Mental Health

Archives of Women’s Mental Health Social Media Editorial Team shared a post on LinkedIn about a paper by Rawan AlSaad et al. publishedin in Archives of Women’s Mental Health:

“New Open-Access Review | Archives of Women’s Mental Health

Can advanced AI help us understand and support women’s mental health across reproductive transitions? This new review explores how multimodal large language models (MLLMs) may reshape reproductive psychiatry.

In this forward-looking review, AlSaad and colleagues examine the potential role of multimodal large language models, a new generation of AI systems capable of integrating text, images, physiological signals, audio, and longitudinal data, in advancing women’s reproductive mental health research and clinical care.

What this review addresses

Women’s mental health risk fluctuates across hormonally mediated reproductive stages, yet clinical tools often fail to integrate the biological, psychological, and social dimensions involved. This article maps how MLLMs could help bridge these gaps by synthesizing complex, multimodal data across the reproductive lifespan.

Seven key application domains explored

Menstruation and premenstrual mood syndromes
Pregnancy and perinatal mental health
Abortion, miscarriage, and recurrent pregnancy loss
The postpartum period
Perimenopause and menopause
Psychiatric comorbidities in infertility
Gynecologic conditions such as endometriosis and polycystic ovary syndrome

What MLLMs could enable

Multimodal risk stratification across reproductive stages
Longitudinal modelling of symptom trajectories
Context-aware clinical decision support
Personalized patient education and self-management tools
Integration of EHRs, hormonal data, wearables, imaging, genetics, and patient-reported outcomes

Critical challenges highlighted

The authors emphasize that responsible deployment requires:

Addressing bias in training datasets
Safeguarding privacy and informed consent for sensitive reproductive data
Ensuring high-quality longitudinal data across life stages
Developing well-governed, standardized multimodal repositories focused on women’s health

Why this matters

This review positions reproductive psychiatry as a domain uniquely suited for multimodal AI approaches and provides a structured framework to guide clinicians, researchers, and technologists toward ethical, equitable, and clinically meaningful integration of AI in women’s mental health care.

Read the full open-access article.”

Title: Multimodal large language models for women’s reproductive mental health

Authors: Rawan AlSaad, Alaa Youssef, Sara Kashani, Majid AlAbdulla, Alaa Abd-alrazaq, Salma M. Khaled, Arfan Ahmed, Javaid Sheikh

Archives of Women's Mental Health