Surgical and hormonal gender-affirming care: cross-domain determinants and artificial intelligence-enabled expansion

AI Summary

GAS and GAHT are evidence-based interventions linked to reduced suicidality, and improved mental health and quality of life for transgender and gender-diverse individuals.
Access to gender-affirming care is limited by structural factors: urban service concentration, insufficient clinician training, fragmented insurance, and rural underrepresentation in research.
Artificial intelligence offers scalable, equity-enhancing solutions: decision support, training simulations, voice therapy, and reduced administrative burden to expand TGD health access.

Int J Equity Health. 2026 May 4. doi: 10.1186/s12939-026-02870-7. Online ahead of print.

ABSTRACT

Gender-affirming surgery (GAS) and gender-affirming hormone therapy (GAHT) and are evidence-based components of care that support the health and well-being of transgender and gender-diverse (TGD) individuals, with extensive evidence linking them to reduced suicidality, improved mental health, and quality of life. Whereas most studies examine isolated outcomes-often within urban populations-this review adopts a system-level, cross-domain perspective to examine how access to gender-affirming care is shaped by structural conditions, including the urban concentration of affirming services, limited clinician training, fragmented insurance coverage, and the underrepresentation of rural TGD populations in research. These intersecting barriers undermine timely initiation, continuity, and quality of GAHT and GAS across diverse care settings. A distinctive contribution of this review is its integration of artificial intelligence (AI) as an emerging dimension of gender-affirming care. By synthesizing evidence on AI-enabled decision support, training simulations, and voice therapy, the review positions AI as a promising, equity-enhancing pathway to expand access, strengthen provider competency, reduce administrative burden, and advance global TGD health equity at scale.

PMID:42083023 | DOI:10.1186/s12939-026-02870-7

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