Psybridge: AI-Augmented Mental Health Assessments for Personalized Therapy

Abstract

The application of artificial intelligence (AI) in mental health care is demonstrating promising potential for early identification, personalized insights, and improved therapy outcomes.

However, existing tools are hindered by language accessibility, the integration of multiple assessments, and interpretability. We conduct a literature-based analysis of existing approaches in personality classification and clinical assessment, identify key gaps, and propose a unified solution that integrates psychological understanding with technological precision. This study lays the groundwork for a flexible and inclusive system to aid therapists in clinical settings.

This research emphasizes the integration of AI- based personality profiling and clinical assessment into one multilingual, privacy-driven system. By combining MBTI-based insights with standardized screeners such as PHQ-9 and GAD-7, PsyBridge offers a unified solution for early identification and personalized therapy recommendations. The proposed framework demonstrates the potential of NLP-powered mental health tools to enhance accessibility and clinical accuracy.