AI-Driven Mental Health Justice Deep Learning Applications for Bipolar Disorder Detection in Institutional Healthcare and Educational Settings

Abstract

AI-driven mental health justice frameworks increasingly ensure early, fair, and accurate detection of bipolar disorder in institutional healthcare and educational environments. This study proposes a deep learning diagnostic model that integrates ethical AI principles to support unbiased mental health assessment, timely intervention, and equitable access to psychological care. The proposed system utilizes a hybrid deep learning architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze multimodal data, including clinical records, behavioral patterns, and academic performance indicators. Researchers trained and validated the model on a dataset of 6,200 anonymized samples from healthcare centers and educational institutions. Experimental results demonstrate a detection accuracy of 96.4%, with a sensitivity of 94.8%, specificity of 95.9%, and an F1-score of 95.3%, outperforming traditional machine learning classifiers by approximately 11.7% in overall accuracy. Cross-validation confirmed the model's robustness with a standard deviation of less than ±1.2% across folds. Additionally, researchers evaluated fairness metrics like demographic parity and equal opportunity, revealing an 18.5% bias reduction compared to baseline models. The system improved early-stage bipolar disorder identification by 32%, enabling proactive mental health support in institutional settings. These findings highlight how AI-driven deep learning models advance mental health justice by enhancing diagnostic precision, reducing systemic bias, and supporting scalable, ethical mental healthcare solutions in clinical and educational domains.