As Large Language Models (LLMs) are increasingly integrated into mental health applications, the prevailing paradigm of cultural neutrality risks creating a technical debt that undermines model generalization and safety. Current systems/frameworks, while focusing on multilingual capabilities, often fail to account for the diverse social and cognitive norms of global populations.This vision paper explores the transition from culturally invariant models toward a research trajectory that operationalizes cultural dimensions such as power distance and individualism as fundamental architectural parameters. Rather than viewing cultural adaptation as a post-hoc fine-tuning task, we advocate for systems that internally encode cultural variability as a primary driver of the model’s reasoning process. We outline a roadmap for architecturally encoding cultural variability as a firstclass parameter in AI/ML pipelines, moving beyond surface-level translation toward systems where cultural dimensions fundamentally shape model reasoning.

Mental health communication in India is linguistically fragmented, culturally diverse, and often underrepresented in clinical NLP. Current health ontologies and mental health resources are dominated by English or Western-centric diagnostic frameworks, leaving a gap in representing patient distress expressions in Indian languages. We propose the Cross-Lingual Graphs of Patient Distress Expressions (CL-PDE), a framework for building cross-lingual mental health ontologies through graph-based methods that capture culturally embedded expressions of distress, align them across languages, and link them with clinical terminology. Our approach addresses critical gaps in healthcare communication by grounding AI systems in culturally valid representations, enabling more inclusive and patient-centric NLP tools for mental health care in multilingual contexts.