MATRIX: Mental heAlth diagnostics Through Real time Intelligent unified X-AI attribution reasoning

Escalating prevalence of mental health issues worldwide has created an unprecedented demand for mental healthcare services, yet the shortage of qualified practitioners limits accessibility for countless individuals in need. AI has emerged as a potential solution to support mental health professionals, offering assistance that goes beyond simple diagnostic aid. This research introduces a novel AI-powered real-time diagnostic support system-MATRIX-for mental healthcare diagnostics designed to interact with users using natural language and utilizing the Patient Health Questionnaire-9 (PHQ-9), a standardized clinical tool for assessing depressive symptoms. The system classifies the interaction into a well-defined checklist and generates the most likely diagnosis through a framework termed X-AI Attribution Reasoning, which provides explainable and attributable diagnostic logic for interdisciplinary clarity. Unlike existing diagnostic support systems that primarily rely on static scoring or predefined rule sets, MATRIX integrates explainable AI (XAI) principles to deliver interpretable reasoning pathways that clinicians can trace and validate. The PHQ-9 implementation within MATRIX has been tested in controlled clinical simulations, confirming its usability and alignment with real-world assessment practices. The system not only accelerates the diagnostic process but also provides transparent explanations, detailed reasoning for such diagnoses, and clinically relevant attributions linked to standard SNOMED Concept IDs, which can be directly utilized by clinicians for documentation, referrals, and electronic health record integration while maintaining data privacy. By offering this level of insight, the system fosters a trustworthy AI-human collaboration that aids clinicians in understanding and validating each diagnostic recommendation. Interpretability within MATRIX is achieved through visual attribution maps and narrative output summaries, ensuring that decision processes remain both transparent and clinically meaningful. The integration of these features enables practitioners to focus on patient care with the assurance that AI-assisted diagnostics align with clinical standards, resulting in reduced time spent per patient and enhanced patient throughput. Our preliminary findings indicate that MATRIX achieves over 89% classification accuracy and high clinician satisfaction in pilot evaluations, demonstrating that AI-driven support systems with explainable, reasonable, and attributable real-time diagnostics can significantly enhance the capacity of mental health services and improve access to timely and effective care for those affected by mental health conditions. This study highlights the essential role of AI in enhancing both the efficiency and trustworthiness of mental health diagnostics in clinical settings, making a compelling case for the integration of AI into modern mental healthcare.