Enhancing Mental Health Outcomes Through AI-Driven Predictive Analytics: A Review of Applications in Neuroscience and Substance Abuse Prevention
1 Department of Pharmacology, Delta State University, Abraka, Nigeria.
2 Centre for Research and Information on Substance Abuse, Uyo, Nigeria.
3 Department of Internal Medicine (Consulting group), Obafemi Awolowo University Health Center, Ile-Ife, Osun, Nigeria.
4 Department of Information Sciences, School of Information Sciences and Engineering, Bay Atlantic University, United States.
5 Department of Optometry, University of Benin, Edo State, Nigeria.
6 Department of Public Health and Health Promotion, School of Pharmacy, Applied Sciences and Public Health, Robert Gordon University, United Kingdom.
Review
International Journal of Biological and Pharmaceutical Sciences Archive, 2025, 10(02), 024-048.
Article DOI: 10.53771/ijbpsa.2025.10.2.0076
Publication history:
Received on 16 August 2025; revised on 14 October 2025; accepted on 16 October 2025
Abstract:
Artificial intelligence (AI)-driven predictive analytics is revolutionizing mental health care by offering groundbreaking solutions for early detection, personalized treatment, and prevention in neuroscience and substance abuse domains. This review meticulously synthesizes cutting-edge applications, demonstrating how machine learning and multimodal data integration uncover biomarkers for psychiatric disorders, forecast neurodegenerative disease progression, and predict substance abuse relapse with remarkable precision. By harnessing diverse data sources—neuroimaging, wearables, and social media—AI empowers clinicians to deliver proactive, tailored interventions, addressing the global mental health crisis with unprecedented scalability. However, challenges like data bias, privacy concerns, and regulatory hurdles underscore the need for ethical frameworks and interdisciplinary collaboration to ensure equitable deployment. Emerging technologies, such as generative models and federated learning, promise to enhance model interpretability and accessibility, particularly for underserved populations. This paper illuminates the transformative potential of AI to redefine mental health outcomes, urging continued innovation to bridge gaps in care and foster a future where precision and compassion converge seamlessly.
Keywords:
AI-Driven Predictive Analytics; Mental Health; Neuroscience; Substance Abuse Prevention; Machine Learning; Neuroimaging; Relapse Prediction; Multimodal Data Integration
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0