MATRIX:Mental HeAlth Diagnostics Through Real Time Intelligent Unified X-AI Attribution Reasoning

Sweety Ramnani^*Kaushik RoyAmit Sheth

• University of South Carolina, Columbia, United States

Escalating prevalence of mental health issues worldwide has created an unprecedented demand for mental health care 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 utilizes Patient Health Questionnaire-9 (PHQ-9), a standardized clinical tool for assessing depressive symptoms and 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 X-AI principles to deliver interpretable reasoning pathways that clinicians can trace and validate. PHQ-9 implementation within MATRIX has been tested in controlled clinical simulations, confirming its usability and alignment with assessment practices. The system not only accelerates the diagnostic process but also provides transparent explanations, detailed reasoning and clinically relevant attributions linked to standard SNOMED Concept IDs which can be directly utilized by clinicians for documentation, referrals, electronic health record (EHR) 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 narrative output summaries, ensuring that decision processes remain transparent and clinically meaningful. Integration of these features allows practitioners to focus on patient care with the assurance that AI-assisted diagnostics align with clinical standards, resulting in reduced time spent on each 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, improving access to timely, effective care for those affected by mental health conditions.