Artificial intelligence in psychiatry: clinical applications, limitations, and ethical challenges

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.