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      Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges

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          Abstract

          Artificial intelligence-based technologies are gradually being applied to psych-iatric research and practice. This paper reviews the primary literature concerning artificial intelligence-assisted psychosis risk screening in adolescents. In terms of the practice of psychosis risk screening, the application of two artificial intelligence-assisted screening methods, chatbot and large-scale social media data analysis, is summarized in detail. Regarding the challenges of psychiatric risk screening, ethical issues constitute the first challenge of psychiatric risk screening through artificial intelligence, which must comply with the four biomedical ethical principles of respect for autonomy, nonmaleficence, beneficence and impartiality such that the development of artificial intelligence can meet the moral and ethical requirements of human beings. By reviewing the pertinent literature concerning current artificial intelligence-assisted adolescent psychosis risk screens, we propose that assuming they meet ethical requirements, there are three directions worth considering in the future development of artificial intelligence-assisted psychosis risk screening in adolescents as follows: nonperceptual real-time artificial intelligence-assisted screening, further reducing the cost of artificial intelligence-assisted screening, and improving the ease of use of artificial intelligence-assisted screening techniques and tools.

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          A Meta-Analysis of Research on Protection Motivation Theory

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            Resting-state connectivity biomarkers define neurophysiological subtypes of depression

            Using functional MRI in a large multisite sample of more that 1,000 patients, four distinct neurophysiological biotypes of depression are defined. These biotypes are used to develop diagnostic classifiers that distinguish patients with depression from controls in separate multisite validation and replication cohorts, and can predict patient responsiveness to therapy.
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              Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial

              Background Web-based cognitive-behavioral therapeutic (CBT) apps have demonstrated efficacy but are characterized by poor adherence. Conversational agents may offer a convenient, engaging way of getting support at any time. Objective The objective of the study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for college students who self-identify as having symptoms of anxiety and depression. Methods In an unblinded trial, 70 individuals age 18-28 years were recruited online from a university community social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content derived from CBT principles in a conversational format with a text-based conversational agent (Woebot) (n=34) or were directed to the National Institute of Mental Health ebook, “Depression in College Students,” as an information-only control group (n=36). All participants completed Web-based versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2). Results Participants were on average 22.2 years old (SD 2.33), 67% female (47/70), mostly non-Hispanic (93%, 54/58), and Caucasian (79%, 46/58). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Intent-to-treat univariate analysis of covariance revealed a significant group difference on depression such that those in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01) while those in the information control group did not. In an analysis of completers, participants in both groups significantly reduced anxiety as measured by the GAD-7 (F1,54= 9.24; P=.004). Participants’ comments suggest that process factors were more influential on their acceptability of the program than content factors mirroring traditional therapy. Conclusions Conversational agents appear to be a feasible, engaging, and effective way to deliver CBT.
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                Author and article information

                Contributors
                Journal
                World J Psychiatry
                WJP
                World Journal of Psychiatry
                Baishideng Publishing Group Inc
                2220-3206
                19 October 2022
                19 October 2022
                : 12
                : 10
                : 1287-1297
                Affiliations
                Graduate School of Education, Peking University, Beijing 100871, China
                School of Education, Tianjin University, Tianjin 300350, China. xinqiaoliu@ 123456pku.edu.cn
                Author notes

                Author contributions: Liu XQ designed the study; Cao XJ and Liu XQ wrote the manuscript and managed the literature analyses; all authors approved the final manuscript.

                Corresponding author: Xin-Qiao Liu, PhD, Associate Professor, School of Education, Tianjin University, No. 135 Yaguan Road, Jinnan District, Tianjin 300350, China. xinqiaoliu@ 123456pku.edu.cn

                Article
                jWJP.v12.i10.pg1287
                10.5498/wjp.v12.i10.1287
                9641379
                36389087
                a0b27f1b-f95f-4066-ac32-6eccd59ff53a
                ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.

                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.

                History
                : 11 July 2022
                : 9 August 2022
                : 22 September 2022
                Categories
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                psychosis risk,adolescents,artificial intelligence,big data,social media,medical ethics,chatbot,machine learning

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