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AI Applications in Depression Detection and Diagnosis: Bibliometric and Visual Analysis of Trends and Future Directions

AI Applications in Depression Detection and Diagnosis: Bibliometric and Visual Analysis of Trends and Future Directions

Similarly, Biological Psychiatry (Biol Psychiatry) demonstrated strong network centrality (0.55), highlighting its importance in computational psychiatry and methodological innovation. The presence of high-impact general science journals, such as Nature and Trends in Cognitive Sciences, along with translational platforms such as Translational Psychiatry (Transl Psychiatry) and Progress in Neuro-Psychopharmacology & Biological Psychiatry, indicates a dynamic flow of ideas across disciplinary boundaries.

Wenbo Ren, Xiali Xue, Lu Liu, Jiahuan Huang

JMIR Ment Health 2025;12:e79293


Speech Emotion Recognition in Mental Health: Systematic Review of Voice-Based Applications

Speech Emotion Recognition in Mental Health: Systematic Review of Voice-Based Applications

Given the role of emotion recognition in identifying psychiatric disorders and the increasing interest in applying SER within clinical psychiatry, where vocal emotional cues offer a noninvasive and objective window into patients’ mental states, this study examines the specific role of emotion recognition in identifying psychiatric disorders.

Eric Jordan, Raphaël Terrisse, Valeria Lucarini, Motasem Alrahabi, Marie-Odile Krebs, Julien Desclés, Christophe Lemey

JMIR Ment Health 2025;12:e74260


Authors’ Response to Peer Reviews of “Challenges in Implementing a Mobile AI Chatbot Intervention for Depression Among Youth on Psychiatric Waiting Lists: Randomized Controlled Study Termination Report”

Authors’ Response to Peer Reviews of “Challenges in Implementing a Mobile AI Chatbot Intervention for Depression Among Youth on Psychiatric Waiting Lists: Randomized Controlled Study Termination Report”

This paper describes the results of a parallel group randomized controlled trial that examined the feasibility of an artificial intelligence (AI) chatbot-led mental health intervention to support pediatric patients on the psychiatry waitlists in Japan. The article is well-written and organized, and the objectives of the study are clearly stated. Methodology elements such as eligibility criteria, information sources, and data collection process are clear.

Junichi Fujita, Yuichiro Yano, Satoru Shinoda, Noriko Sho, Masaki Otsuki, Akira Suda, Mizuho Takayama, Tomoko Moroga, Hiroyuki Yamaguchi, Mio Ishii, Tomoyuki Miyazaki

JMIRx Med 2025;6:e82083


Peer Review of “Challenges in Implementing a Mobile AI Chatbot Intervention for Depression Among Youth on Psychiatric Waiting Lists: Randomized Controlled Study Termination Report”

Peer Review of “Challenges in Implementing a Mobile AI Chatbot Intervention for Depression Among Youth on Psychiatric Waiting Lists: Randomized Controlled Study Termination Report”

This paper [1] describes the results of a parallel group randomized controlled trial that examined the feasibility of an artificial intelligence (AI) chatbot-led mental health intervention to support pediatric patients on the psychiatry waitlists in Japan. The article is well-written and organized, and the objectives of the study are clearly stated. Methodology elements such as eligibility criteria, information sources, and data collection process are clear.

Maria da Graca Ambrosio

JMIRx Med 2025;6:e82073


Challenges in Implementing a Mobile AI Chatbot Intervention for Depression Among Youth on Psychiatric Waiting Lists: Randomized Controlled Study Termination Report

Challenges in Implementing a Mobile AI Chatbot Intervention for Depression Among Youth on Psychiatric Waiting Lists: Randomized Controlled Study Termination Report

The intervention group received general mental health information via the Yokohama City University Child Psychiatry Department’s website, “Oyako-no Kokoro-no Tomarigi” [22], which provides short video programs and texts that provide easy-to-understand explanations of common mental health issues for children and adolescents. In addition, 8 weekly sessions were provided by emol. Each session lasted between 20 and 30 minutes.

Junichi Fujita, Yuichiro Yano, Satoru Shinoda, Noriko Sho, Masaki Otsuki, Akira Suda, Mizuho Takayama, Tomoko Moroga, Hiroyuki Yamaguchi, Mio Ishii, Tomoyuki Miyazaki

JMIRx Med 2025;6:e70960


Changes in Documentation After Implementing Open Notes in Mental Health Care: Pre-Post Mixed Methods Study

Changes in Documentation After Implementing Open Notes in Mental Health Care: Pre-Post Mixed Methods Study

The domain of psychiatry and mental health care represents a particularly compelling context for the adoption of open notes. Patients in these settings often face societal stigma and, in some cases, coercion during treatment [10,11]. Thus, they may derive considerable benefit from increased transparency and involvement in their care. Studies suggest that open notes can foster trust in HCPs, enhance health literacy, and promote greater empowerment among psychiatric patients [12].

Eva Meier-Diedrich, Charlotte Blease, Martin Heinze, Jonas Wördemann, Julian Schwarz

J Med Internet Res 2025;27:e72667


Performance of Open-Source Large Language Models in Psychiatry: Usability Study Through Comparative Analysis of Non-English Records and English Translations

Performance of Open-Source Large Language Models in Psychiatry: Usability Study Through Comparative Analysis of Non-English Records and English Translations

Multilingual applicability is another critical consideration in the use of LLMs in the field of psychiatry. LLMs are primarily trained on English data, with relatively less exposure to non-English data [14]. This raises questions about LLMs’ ability to deal with languages other than English in psychiatry, where subtle nuances and cultural influences are important.

Min-Gyu Kim, Gyubeom Hwang, Junhyuk Chang, Seheon Chang, Hyun Woong Roh, Rae Woong Park

J Med Internet Res 2025;27:e69857