AI-Driven Mental Health Diagnosis: Early Detection of Psychological Disorders

Abstract

This research explores the potential of Artificial Intelligence (AI) in revolutionizing the early detection and diagnosis of mental health disorders, thereby addressing the critical limitations of traditional clinical methods. Mental health disorders, such as depression, anxiety, and schizophrenia, often go undiagnosed in their early stages due to subjective assessment tools and resource constraints, leading to delays in treatment and poor patient outcomes. AI, particularly through sophisticated machine learning (ML) and natural language processing (NLP) models, offers a promising data-driven solution for more objective, accurate, and timely diagnoses. The study proposes a novel AI-driven system that analyses a fusion of multimodal data inputs, including vocal prosody and speech patterns, transcribed patient interviews for semantic and syntactic anomalies, and digital behavioural indicators from wearables and smartphones. By identifying subtle, subclinical signs of psychological disorders before they fully manifest, this system aims to empower clinicians with predictive insights. The research validates this approach through extensive evaluation, benchmarking the AI's performance against standard diagnostic criteria. The results demonstrate a significant improvement in both diagnostic accuracy and sensitivity, underscoring AI's potential as a scalable and cost-effective decision-support tool that can enhance the effectiveness of early intervention strategies in mental healthcare.

JEL Codes: I12, I18, C63, D83, J24.

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