AI-Driven Mental Health Intelligence and Early Intervention Framework: An Enterprise Platform Architecture Perspective

Authors

  • Muneer Basha Shaik

Keywords:

Multimodal AI Integration, Mental Health Monitoring, Enterprise Platform Architecture, Event-Driven Processing, Federated Learning, Explainable AI

Abstract

With 1․17 billion people worldwide with mental health issues‚ there is a meaningful gap between clinical demand and the availability of production-grade smart monitoring systems․ We present an enterprise platform architecture for AI-based mental health intelligence and early intervention systems‚ from the standpoint of large-scale distributed systems engineering‚ rather than a new class of clinical algorithms․ The architecture is based on event-driven multimodal data pipelines‚ LLM reasoning layers with retrieval-augmented generation (RAG)‚ federated learning‚ and a Digital Mental Health Twin construct that creates a continuously updated behavioral and psychological profile per individual․ Explainability components help clinicians to see the reasoning behind outputs‚ while retaining human diagnostic authority․ The architecture allows for early recognition of risk․ Edge-cloud hybrid deployment patterns address the latency and data-sensitivity constraints of mental health applications․ The holistic architecture is qualitatively evaluated against common non-functional requirements missing in existing health AI proposals: scalability‚ compatibility with privacy regulations‚ interpretability‚ and clinical trust․ As an enterprise integration problem‚ the architecture acts as a deployable framework that healthcare organizations‚ workplace wellness programs‚ schools‚ and veteran services may implement to scale AI-informed preventive mental healthcare in a manner compatible with the culture of practice.

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Published

10.08.2026

How to Cite

Muneer Basha Shaik. (2026). AI-Driven Mental Health Intelligence and Early Intervention Framework: An Enterprise Platform Architecture Perspective. International Journal of Intelligent Systems and Applications in Engineering, 14(1s), 2153 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8496

Issue

Vol. 14 No. 1s (2026)

Section

Research Article

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