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Digital Mental Health Through an Intersectional Lens: A Narrative Review
Rose Yesha1,2, Max C E Orezzoli3, Kimberly Sims4,5
1MedStar Health Research Institute, Columbia, MD 21044, USA.
Healthcare (Basel, Switzerland)
|January 28, 2026
Summary
Digital mental health applications (DMHAs) using artificial intelligence (AI) can improve care but risk bias. An intersectional framework highlights culturally responsive strategies to ensure equitable AI in mental healthcare for marginalized groups.
Area of Science:
- Digital mental health
- Artificial intelligence in healthcare
- Health equity
Background:
- Individuals with mental illness facing marginalization experience compounded discrimination and inadequate care.
- Digital mental health applications (DMHAs) leverage AI and machine learning, but may perpetuate bias.
- Algorithmic bias in DMHAs exacerbates inequities for at-risk, marginalized populations.
Purpose of the Study:
- To analyze current literature on digital mental health using an intersectional framework.
- To identify culturally responsive strategies for equitable AI in mental healthcare.
- To address the disproportionate mental health challenges faced by marginalized individuals.
Main Methods:
- Narrative literature review.
- Application of an intersectional framework to analyze digital mental health.
- Assessment of AI training models and their representation of marginalized populations.
Main Results:
- DMHAs can identify distress and resilience markers but risk algorithmic bias.
- Marginalized populations face significant barriers to culturally competent digital mental healthcare.
- An intersectional approach reveals the need for AI models reflecting diverse lived experiences.
Conclusions:
- Equitable digital mental health solutions require AI training data that represent marginalized communities.
- Culturally responsive strategies are crucial to mitigate bias, harm, and exclusion in DMHAs.
- Incorporating lived experiences into AI development promotes fairness and improves outcomes for all.
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