Okpete UE, Byeon H. Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment. World J Psychiatry 2024; 14(8): 1148-1164 [PMID: 39165556 DOI: 10.5498/wjp.v14.i8.1148]
Corresponding Author of This Article
Haewon Byeon, DSc, PhD, Associate Professor, Director, Department of Digital Anti-aging Healthcare (BK21), Inje University, No. 197 Injero, Gyeonsangnamdo, Gimhae 50834, South Korea. bhwpuma@naver.com
Research Domain of This Article
Psychiatry
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Aug 19, 2024; 14(8): 1148-1164 Published online Aug 19, 2024. doi: 10.5498/wjp.v14.i8.1148
Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment
Uchenna Esther Okpete, Haewon Byeon
Uchenna Esther Okpete, Haewon Byeon, Department of Digital Anti-aging Healthcare (BK21), Inje University, Gimhae 50834, South Korea
Haewon Byeon, Department of Medical Big Data, Inje University, Gimhae 50834, South Korea
Author contributions: Byeon H and Okpete UE designed the study; Okpete UE involved in data interpretation; Byeon H developed methodology; Okpete UE performed the statistical analysis, and assisted with writing the article.
Supported byBasic Science Research Program through the National Research Foundation of Korea Funded by the Ministry of Education, No. NRF-RS-2023-00237287, and No. NRF-2021S1A5A8062526; and Local Government-University Cooperation-Based Regional Innovation Projects, No. 2021RIS-003.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: Https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Haewon Byeon, DSc, PhD, Associate Professor, Director, Department of Digital Anti-aging Healthcare (BK21), Inje University, No. 197 Injero, Gyeonsangnamdo, Gimhae 50834, South Korea. bhwpuma@naver.com
Received: April 4, 2024 Revised: June 13, 2024 Accepted: July 9, 2024 Published online: August 19, 2024 Processing time: 129 Days and 20.7 Hours
Abstract
Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical, genetic, environmental, and lifestyle factors to optimize medication management. This study investigates how artificial intelligence (AI) and machine learning (ML) can address key challenges in integrating pharmacogenomics (PGx) into psychiatric care. In this integration, AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions. AI-driven models integrating genomic, clinical, and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder. This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry, highlighting the importance of ethical considerations and the need for personalized treatment. Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care. Future research should focus on developing enhanced AI-driven predictive models, privacy-preserving data exchange, and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.
Core Tip: This paper explores the convergence of precision medicine and artificial intelligence (AI) in psychiatric care, focusing on tailoring treatments to individuals' genetic backgrounds. It underscores the complexity of psychiatric disorders, attributed to varied genetic, environmental, and lifestyle factors, and the role of AI in navigating these challenges by analyzing large genomic datasets. Despite obstacles such as data privacy, computational requirements, and model generalization, the study highlights the necessity for ethical guidelines and regulatory frameworks for AI use in psychiatric genetics. Furthermore, it stresses the importance of interdisciplinary collaboration to effectively address the AI-related implementation challenges in precision medicine.
