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©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Meta-Anal. Apr 18, 2023; 11(4): 79-91
Published online Apr 18, 2023. doi: 10.13105/wjma.v11.i4.79
Published online Apr 18, 2023. doi: 10.13105/wjma.v11.i4.79
Artificial intelligence ecosystem for computational psychiatry: Ideas to practice
Xin-Qiao Liu, Xin-Yu Ji, Yi-Fan Zhang, School of Education, Tianjin University, Tianjin 300350, China
Xing Weng, Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
Author contributions: Liu XQ designed the study; Liu XQ, Ji XY, Weng X and Zhang YF wrote the manuscript and conducted the literature analyses; All of the authors contributed equally to this work and have approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Xin-Qiao Liu, PhD, Associate Professor, School of Education, Tianjin University, No. 135 Yaguan Road, Jinnan District, Tianjin 300350, China. xinqiaoliu@pku.edu.cn
Received: December 26, 2022
Peer-review started: December 26, 2022
First decision: March 9, 2023
Revised: March 18, 2023
Accepted: April 4, 2023
Article in press: April 4, 2023
Published online: April 18, 2023
Processing time: 108 Days and 21.7 Hours
Peer-review started: December 26, 2022
First decision: March 9, 2023
Revised: March 18, 2023
Accepted: April 4, 2023
Article in press: April 4, 2023
Published online: April 18, 2023
Processing time: 108 Days and 21.7 Hours
Core Tip
Core Tip: This study reviews and integrates the methods and models in the clinical practice of computational psychiatry and constructs a complete and mature Artificial Intelligence ecosystem. The ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources to obtain a more complete understanding of mental health conditions. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection.