Psychological Disord Ers Diagnosis Framework Using Reflective Listening and Generative AI

Abstract

Mental health disorders such as Generalized Anxiety Disorder (GAD), Major Depressive Disorder (MDD), and Borderline Personality Disorder (BPD) are rising globally, and this causes access to clinically trained professionals limited. To address this gap, our research proposes an innovative Psychological Disorder Diagnosis Framework Using Reflective Listening and Generative AI, integrating multimodal, natural language processing, DSM-5 diagnostic logic, knowledge-graph based reasoning, and validated psychometric assessments under one system. The system conducts a multi-stage evaluation process that starts with two rounds of conversational screening, followed by NLP-driven symptom extraction, keyword clustering, probability scoring, and DSM-5 validation checks. The reflective listening technique is used in fine-tuned LLM-based empathetic dialogue to enhance emotional understanding and user comfort. A secondary diagnostic stage executes standardized clinical tests, including the Penn State Worry Questionnaire (PSWQ) for GAD, Beck Depression Inventory (BDI) for MDD, and McLean Screening Instrument (MSI) for BPD, ensuring that AI predictions are verified against clinical standards. A comprehensive Gold dataset was created through system-generated session logs, enhanced with DSM-5 ground-truth labels, validated psychometric scores, corrected knowledge-graph structures, and reflective-listening quality ratings provided by licensed psychologists. This dataset supports the fine-tuning of a generative model capable of producing empathetic reflections, accurate disorder predictions, DSM-5 aligned reasoning, and appropriate treatment suggestions. The architecture combines a user-friendly multilingual interface, text and audio inputs, an NLP and reflective-listening engine, a clinical rule-based inference module, and a visualization layer that provides probability scores, knowledge graphs, and diagnostic summaries. Results determine that the proposed framework enhances diagnostic transparency, cultural adaptability, and clinical validity compared to traditional sentiment-based mental health models. The system achieves high stability between AI predictions and clinician evaluations, while reflective listening significantly improves user engagement and emotional coherence. This research contributes an explainable, empathetic, and clinically grounded AI diagnostic framework, establishing a foundation for next-generation intelligent mental health assessment systems suitable for real-world psychological support and early screening applications.