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Artificial intelligence in psychiatry: clinical applications; limitations; and ethical challenges
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DOI：10.3389/fnbeh.2026.1864429.png)
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Abstract
En 中文
Artificial intelligence (AI) is rapidly transforming psychiatric research and clinical practice; offering new capabilities in areas such as diagnosis; risk prediction; digital phenotyping; and treatment personalization. In the domain of diagnostic classification; machine learning models have demonstrated classification accuracy across major psychiatric disorders in internally validated research settings. In a distinct and non-equivalent domain; large language model–assisted clinical decision support has shown performance comparable to expert clinicians in a specific; structured benchmark task; this finding should not be generalized to open-ended clinical practice. However; this technological promise is shadowed by profound methodological; clinical; and ethical limitations. The majority of AI models in neuroimaging-based psychiatry carry a high risk of bias; external validation remains rare; and evidence of real-world clinical impact is scarce. Critically; the field is developing in a context where vast repositories of sensitive mental health data are increasingly controlled by large technology corporations. This trend raises urgent; yet underexplored; questions about data governance and commercial use; as well as broader concerns around accountability and long-term behavioral surveillance. Furthermore; the reliance of AI systems on statistical distributions to define normality risks encoding a historically unstable and culturally contingent concept as a medical standard; with particular consequences for the pathologization of human diversity. This perspective article argues that the psychiatric community must assume an active governance role; advocating for patient-centered data frameworks that do not reduce human suffering to a monetizable data stream.
Keywords:
artificial intelligence
psychiatry
digital phenotyping
data governance
algorithmic bias
mental health ethics
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