Neuro Symbolic AI in personalized mental health therapy: Bridging cognitive science and computational psychiatry
Department of Computer Science, Maharishi International University, USA.
Review Article
World Journal of Advanced Research and Reviews, 2023, 19(02), 1663-1679
Publication history:
Received on 17 June 2023; revised on 25 August 2023; accepted on 28 August 2023
Abstract:
Personalized mental health therapy has gained increasing attention as advancements in artificial intelligence (AI) enable tailored treatment strategies based on individual cognitive and emotional profiles. Neuro-symbolic AI, a hybrid approach combining symbolic reasoning and neural networks, offers a promising solution for bridging cognitive science and computational psychiatry. Unlike conventional AI models that rely solely on deep learning, neuro-symbolic AI integrates human-interpretable knowledge representations with data-driven learning, enhancing the adaptability and explainability of AI-driven mental health interventions. This study explores the role of neuro-symbolic AI in revolutionizing personalized mental health care by integrating cognitive theories, structured knowledge graphs, and deep learning-based predictive modeling. By leveraging structured symbolic reasoning alongside probabilistic inference, neuro-symbolic systems enhance diagnostic accuracy, facilitate adaptive therapy recommendations, and improve patient-clinician interactions. Applications include AI-assisted cognitive behavioral therapy (CBT), personalized mood stabilization strategies, and early detection of mental health disorders through multimodal data fusion from speech, facial expressions, and physiological biomarkers. Furthermore, we examine the advantages of neuro-symbolic AI in addressing key challenges in computational psychiatry, including model interpretability, causal reasoning in mental health diagnosis, and the integration of psychological theories into AI frameworks. A comparative analysis of neuro-symbolic AI versus purely neural-based models highlights its superior capacity for reasoning, transparency, and personalized therapeutic adaptation. Future directions focus on refining hybrid AI architectures, integrating real-time patient feedback for dynamic therapy adjustment, and addressing ethical concerns related to AI-driven mental health interventions.
Keywords:
Neuro-Symbolic AI; Computational Psychiatry; Personalized Therapy; Cognitive Science; Mental Health Ai; Hybrid Ai Models
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0