Liu XQ, Wang X, Zhang HR. Large multimodal models assist in psychiatry disorders prevention and diagnosis of students. World J Psychiatry 2024; 14(10): 1415-1421 [DOI: 10.5498/wjp.v14.i10.1415]
Corresponding Author of This Article
Xin-Qiao Liu, PhD, Associate Professor, School of Education, Tianjin University, No. 135 Yaguan Road, Jinnan District, Tianjin 300350, China. xinqiaoliu@pku.edu.cn
Research Domain of This Article
Psychiatry
Article-Type of This Article
Editorial
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. Oct 19, 2024; 14(10): 1415-1421 Published online Oct 19, 2024. doi: 10.5498/wjp.v14.i10.1415
Large multimodal models assist in psychiatry disorders prevention and diagnosis of students
Xin-Qiao Liu, Xin Wang, Hui-Rui Zhang
Xin-Qiao Liu, Xin Wang, School of Education, Tianjin University, Tianjin 300350, China
Hui-Rui Zhang, Faculty of Education, The Open University of China, Beijing 100039, China
Author contributions: Liu XQ and Zhang HR designed the study; Liu XQ, Wang X and Zhang HR wrote the manuscript; All authors have read and 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: April 10, 2024 Revised: September 3, 2024 Accepted: September 25, 2024 Published online: October 19, 2024 Processing time: 190 Days and 1.2 Hours
Abstract
Students are considered one of the groups most affected by psychological problems. Given the highly dangerous nature of mental illnesses and the increasingly serious state of global mental health, it is imperative for us to explore new methods and approaches concerning the prevention and treatment of mental illnesses. Large multimodal models (LMMs), as the most advanced artificial intelligence models (i.e. ChatGPT-4), have brought new hope to the accurate prevention, diagnosis, and treatment of psychiatric disorders. The assistance of these models in the promotion of mental health is critical, as the latter necessitates a strong foundation of medical knowledge and professional skills, emotional support, stigma mitigation, the encouragement of more honest patient self-disclosure, reduced health care costs, improved medical efficiency, and greater mental health service coverage. However, these models must address challenges related to health, safety, hallucinations, and ethics simultaneously. In the future, we should address these challenges by developing relevant usage manuals, accountability rules, and legal regulations; implementing a human-centered approach; and intelligently upgrading LMMs through the deep optimization of such models, their algorithms, and other means. This effort will thus substantially contribute not only to the maintenance of students’ health but also to the achievement of global sustainable development goals.
Core Tip: Large multimodal models represented by ChatGPT have become a new approach for diagnosing, treating, and addressing students’ mental health issues. However, there are, notably, both opportunities and challenges in the diagnosis and prevention of mental disorders by students. To unleash the full potential of large multimodal models and truly achieve the empowerment of psychological well-being through technology, it is necessary to obtain a correct understanding of their strengths and weaknesses and to continuously explore the organic integration of artificial intelligence and students’ mental health.
