AI for mental health: clinician expectations and priorities in computational psychiatry
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Springer Nature
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.
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Except where otherwise noted, this item's license is described as Attribution 4.0 International