The finding:
This article reviews the current landscape and emerging applications of artificial intelligence (AI) in psychiatry. The authors highlight the growing role of AI in diagnostics, risk prediction, symptom monitoring, and clinical decision support. They note that while machine learning algorithms show promise in identifying patterns in large datasets (e.g., EHRs, imaging, and speech analysis), real-world clinical integration remains limited. Ethical concerns and algorithmic bias are also discussed as critical considerations moving forward

Strength and weaknesses:
A strength of the article is its comprehensive overview of various AI applications—from chatbots and diagnostic tools to predictive modeling and natural language processing. It delineates the technical underpinnings while remaining accessible to clinicians without a background in machine language learning. However, the article largely focuses on outpatient and research settings, with limited discussion of implementation in acute care or medically ill populations. There is also little evaluation of the current regulatory landscape or barriers to institutional adoption.

Relevance:
For Consultation-Liaison psychiatrists, this review is a valuable introduction to AI tools that may soon impact inpatient and medical-surgical psychiatry settings. Applications such as automated risk assessment, passive symptom monitoring, and decision support systems could enhance care for complex hospitalized patients. Awareness of the technology’s limitations and ethical implications is also crucial, particularly as health systems begin to pilot AI-assisted psychiatric tools in general medical environments.