Aug 2026· Journal of Psychiatry and Psychological Sciences· pp. 182· 0 citations
TL;DR
This review synthesises current developments in AI based psychiatric applications, examining diagnostic innovations, predictive modelling, and emerging treatment personalisation strategies.
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.
These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets.
Juster Donal Sinaga· Journal of Society Counselin...· 0 citations
Artificial intelligence is being considered as a means of assisting psychiatric diagnosis, risk prediction, and long-term monitoring. Such applications in psychiatry have clinical relevance due to the fact that most psychiatric diagnosis is still based on interview, observation, and self-report, and also because it rel...
R. Wojtan, A. Szeszko, W. Czernek et al.· International Journal of Inn...· 0 citations
This review examines how AI and emerging technologies may contribute to mental health care across diagnostic assessment, biomarker development, treatment selection, personalized neuromodulation, adaptive interventions, and conversational agents.
N. Qiu, Duo-Duo Xu, B. Sahakian et al.· Psychotherapy and Psychosoma...· 0 citations
This critical narrative review examines evidence across the three domains in which artificial intelligence has been most extensively applied to mental health, namely diagnostic classification and risk detection, treatment personalisation, and continuous patient monitoring, and asks why demonstrated technical performanc...
Oyebode Mary Oluwabunmi, Anyebe Daniel Ameh, Jacob Miracle Godswill et al.· Journal of medicine and heal...· 0 citations
AI in psychiatric diagnostics has demonstrated transformative potential, particularly in the areas of NLP and digital phenotyping, but current neuroimaging models require methodological improvements.
Wiktor Rybicki, Radosław Dutczak, Aleksandra Sobieska et al.· International Journal of Inn...· 0 citations
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.
Esteban Zavaleta-Monestel, L. Herrera-Jiménez, Sofía Suárez-Sánchez et al.· Psychiatric Research and Cli...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
