Artificial Intelligence in Psychiatric Diagnosis and Treatment Planning: A Comprehensive Review

Abstract

Artificial intelligence (AI) has become increasingly prominent in psychiatric research and clinical practice, offering new approaches to diagnosis, risk stratification, and personalised treatment planning. Advances in machine learning, digital phenotyping, and multimodal data integration have enabled tools capable of analysing complex behavioural, clinical, and neurobiological information. This review synthesises current developments in AI based psychiatric applications, examining diagnostic innovations, predictive modelling, and emerging treatment personalisation strategies. While reported accuracies and predictive performance are encouraging, the field remains constrained by methodological variability, limited external validation, and challenges related to transparency, ethics, and clinical implementation. Future progress will depend on rigorous validation, harmonised reporting standards, and integration of AI systems into real world clinical workflows.