Assessing Whether Women's Unique Needs and Persistent Disparities in Mental Health Care Are Being Addressed by Emerging AI-Powered Digital Health Solutions

Abstract

Background: Mental health is a growing challenge, with women and girls disproportionately affected by conditions including anxiety and depression. These disparities are driven and exacerbated by factors such as socioeconomic status, race, and access to healthcare. Standards of care for these conditions vary, including combinations of in- and out-patient psychotherapy, pharmacotherapy, and behavioral therapies. Patients’ access to care is impacted by multifaceted system and individual level challenges, including provider shortages, cost, health literacy, and scheduling, with minority, economically disadvantaged, and neurodiverse women facing even greater barriers. Innovative digital health tools using artificial intelligence (AI) have the potential to address care gaps and narrow disparities for women and other vulnerable groups.

Methods: A systematic review of available technologies was conducted to identify current AI solutions and digital health interventions focused on depression, anxiety, or overall mental wellbeing. A solution taxonomy was developed based on similar AI-enabled services. The solutions were categorized based on intervention modality, disease focus, target population, realized outcomes, and business model. The solution’s intent and ability to address mental health challenges for women was then assessed.

Results: Current patient-facing mental health solutions include conversational AI support, healthy habit formation support, personalized care plans, screening tools, biometric risk factor identification, and digital music therapy. Among the examined technologies, 3 of 23 (13%) were designed for women and girls, and two of those solutions focused on women’s reproductive years. Just over half of assessed solutions (52%) use business-to-business-centric business models, requiring employers, payers, or providers to enable access.

Conclusions: AI-driven approaches can complement traditional mental health services by providing targeted education, improved access, and personalized care. Despite the potential to reduce gender disparities in mental healthcare, most AI solutions do not specifically address the needs of women and girls. Opportunities for greater impact include tailoring interventions to better suit women from diverse backgrounds, improving access through business model innovation, and expanding clinical integration in alignment with evidence-based treatment. Future research should focus on the long-term impact of AI interventions on mental health outcomes, designing specific tools for women and girls, and the risk of exacerbating biases by using AI in mental healthcare.