Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges

Abstract

Objective This review examined the historical development of artificial intelligence (AI) in psychiatry from 1972 to 2025 and identified persistent barriers to clinical translation. Method The review used a structured source‐identification approach across PubMed/MEDLINE, Web of Science, Google Scholar, ScienceDirect, citation tracking, and targeted historical searches. It synthesized sources qualitatively and organized them chronologically. A structured evidence table summarized representative milestones by era, AI paradigm, clinical task, data modality, validation approach, implementation status, and translational limitation. Results Psychiatric AI evolved from symbolic simulation and rule‐based expert systems to connectionist models, supervised machine learning, computational psychiatry, digital phenotyping, multimodal monitoring, digital mental health tools, and large language models. Despite increasing computational sophistication, recurring barriers persisted, including uncertain target validity, diagnostic heterogeneity, limited external validation, poor transportability, interpretability challenges, workflow integration, equity, patient trust, and governance. Across eras, technical progress was cyclical rather than linear, with successive waves reproducing unresolved clinical and implementation challenges. Conclusions The clinical impact of psychiatric AI will likely depend less on algorithmic novelty alone than on clearer clinical targets, prospective validation, implementation trials, patient‐centered evaluation, equity‐sensitive generalizability, and mental health–specific governance. Relevance to Clinical Practice For routine psychiatric care, AI tools require evidence of clinical validity, transportability, workflow compatibility, patient acceptability, equity, and appropriate governance rather than technical performance alone.