@article{10.37349/eds.2026.1008165,
abstract = {Schizophrenia affects approximately 24 million people worldwide, representing 0.3–0.7% of the global population, and remains a leading cause of years lived with disability. The disorder contributes to over 13 million disability-adjusted life years, underscoring its substantial global health and socioeconomic burden. This review critically examines the convergence of artificial intelligence (AI) and advanced oral drug delivery systems as an emerging strategy in schizophrenia management. Conventional diagnostic frameworks, reliant on subjective symptom assessment, often result in delayed or inaccurate diagnosis, while standard oral antipsychotics are limited by poor bioavailability, extensive first-pass metabolism, and inadequate brain targeting. Recent advances in AI, including natural language processing (NLP), neuroimaging analytics, electrophysiological modeling, and multi-omics integration, support diagnostic classification, risk prediction, symptom monitoring, and patient stratification. Simultaneously, nanotechnology-driven oral delivery platforms such as lipid nanoparticles, dendrimers, and proliposomes enhance pharmacokinetics, central nervous system targeting, and therapeutic adherence. The integration of AI with pharmacogenomics, wearable monitoring, and digital twin models further facilitates real-time dose optimization and personalized therapy. Despite promising preclinical and clinical outcomes, challenges related to data privacy, algorithmic bias, scalability, and regulatory translation persist. This review highlights a shift toward precision psychiatry, where AI-enabled diagnostics and smart oral therapeutics may support predictive, personalized, and adaptive care in schizophrenia.},
author = {Dudhwala, Yash D. and Kapoor, Devesh U. and Mehta, Riya K. and Shah, Ketan and Nayak, Visha M.},
doi = {10.37349/eds.2026.1008165},
journal = {Exploration of Drug Science},
elocation-id = {1008165},
title = {AI-integrated nanotherapeutics for schizophrenia: advancing precision oral drug delivery},
url = {https://www.explorationpub.com/Journals/eds/Article/1008165},
volume = {4},
year = {2026}
}