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AI for mental health: clinician expectations and priorities in computational psychiatry

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Title: AI for mental health: clinician expectations and priorities in computational psychiatry
Authors: Leo Fischer, Paula Antonia Mann, Minh-Hieu H. Nguyen, Stefan Becker, Shiva Khodadadi, Antonia Schulz, Sharmili Edwin Thanarajah, Jonathan Repple, Tim Hahn, Andreas Reif, Amir Salamikhanshan, Sarah Kittel-Schneider, Winfried Rief, Christoph Mulert, Stefan G. Hofmann, Udo Dannlowski, Tilo Kircher, Felix P. Bernhard, Hamidreza Jamalabadi
Source: BMC Psychiatry, Vol 25, Iss 1, Pp 1-8 (2025)
Publisher Information: BMC, 2025.
Publication Year: 2025
Collection: LCC:Psychiatry
Subject Terms: Computational psychiatry, Ecological momentary assessment (EMA), AI, Clinician expectations, Psychiatry, RC435-571
Description: Abstract Mental disorders represent a major global health challenge, with an estimated lifetime prevalence approaching 30%. Despite the availability of effective treatments, access to mental health care remains inadequate. Computational psychiatry, leveraging advancements in artificial intelligence (AI) and machine learning (ML), has shown potential for transforming mental health care by improving diagnosis, prognosis, and the personalization of treatment. However, integrating these technologies into routine clinical practice remains limited due to technical and infrastructure challenges. While ongoing computational developments will enhance AI’s precision, many studies focus on its broad potential without providing specific, clinician-informed guidance for immediate application. To address this gap and the urgent need for clinically actionable AI tools, we surveyed 53 psychiatrists and clinical psychologists to identify their priorities for AI in mental health care. Our findings reveal a strong preference for tools enabling continuous monitoring and predictive modeling, particularly in outpatient settings. Clinicians prioritize accurate predictions of symptom trajectories and proactive patient monitoring over interpretability and explicit treatment recommendations. Self-reports, third-party observations, and sleep quality and duration emerged as key data inputs for effective models. Together, this study provides a clinician-driven roadmap for AI integration, emphasizing predictive models based on ecological momentary assessment (EMA) data to forecast disorder trajectories and support real-world practice.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1471-244X
Relation: https://doaj.org/toc/1471-244X
DOI: 10.1186/s12888-025-06957-3
Access URL: https://doaj.org/article/902755d0e5a24c9bbc175cb860d5640b
Accession Number: edsdoj.902755d0e5a24c9bbc175cb860d5640b
Database: Directory of Open Access Journals
Description
Abstract:Abstract Mental disorders represent a major global health challenge, with an estimated lifetime prevalence approaching 30%. Despite the availability of effective treatments, access to mental health care remains inadequate. Computational psychiatry, leveraging advancements in artificial intelligence (AI) and machine learning (ML), has shown potential for transforming mental health care by improving diagnosis, prognosis, and the personalization of treatment. However, integrating these technologies into routine clinical practice remains limited due to technical and infrastructure challenges. While ongoing computational developments will enhance AI’s precision, many studies focus on its broad potential without providing specific, clinician-informed guidance for immediate application. To address this gap and the urgent need for clinically actionable AI tools, we surveyed 53 psychiatrists and clinical psychologists to identify their priorities for AI in mental health care. Our findings reveal a strong preference for tools enabling continuous monitoring and predictive modeling, particularly in outpatient settings. Clinicians prioritize accurate predictions of symptom trajectories and proactive patient monitoring over interpretability and explicit treatment recommendations. Self-reports, third-party observations, and sleep quality and duration emerged as key data inputs for effective models. Together, this study provides a clinician-driven roadmap for AI integration, emphasizing predictive models based on ecological momentary assessment (EMA) data to forecast disorder trajectories and support real-world practice.
ISSN:1471244X
DOI:10.1186/s12888-025-06957-3