Envisioning an AI-Enhanced Mental Health Ecosystem
This work tackles the problem of inadequate mental health support access, particularly for underserved populations, by envisioning a hybrid AI-human ecosystem, though it appears incremental as it builds on existing AI approaches without introducing a new paradigm.
The paper addresses the global mental health crisis by proposing an AI-enhanced ecosystem to complement human-led interventions, aiming to improve access and support for underserved populations through scalable and context-aware AI applications.
The rapid advancement of Large Language Models (LLMs), reasoning models, and agentic AI approaches coincides with a growing global mental health crisis, where increasing demand has not translated into adequate access to professional support, particularly for underserved populations. This presents a unique opportunity for AI to complement human-led interventions, offering scalable and context-aware support while preserving human connection in this sensitive domain. We explore various AI applications in peer support, self-help interventions, proactive monitoring, and data-driven insights, using a human-centred approach that ensures AI supports rather than replaces human interaction. However, AI deployment in mental health fields presents challenges such as ethical concerns, transparency, privacy risks, and risks of over-reliance. We propose a hybrid ecosystem where where AI assists but does not replace human providers, emphasising responsible deployment and evaluation. We also present some of our early work and findings in several of these AI applications. Finally, we outline future research directions for refining AI-enhanced interventions while adhering to ethical and culturally sensitive guidelines.