Artificial intelligence in psychiatry: transforming diagnosis, risk prediction and personalized mental healthcare – a narrative review

Rehana Banoo; Shilki Sharma; Nathiya K.; Smriti G. Solomon; Pooja Shree Mazumdar; T. Aruna; Mayanglambam Sankerdev Singh; Mohammed Umar; Simon Samuvel S.; P. Emmanuel Raju

doi:10.18203/2320-6012.ijrms20263570

Authors

Rehana Banoo Department of Nursing, Government Nursing College, Government Medical College (GMC) Doda, University of Jammu, Doda, Jammu and Kashmir, India
Shilki Sharma Department of Mental Health Nursing, Saraswathi College of Nursing, Hapur, Atal Bihari Vajpayee Medical University (ABVMU), Lucknow, Uttar Pradesh, India
Nathiya K. Department of Mental Health Nursing, (Research Scholar) Saveetha College of Nursing, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India
Smriti G. Solomon Department of Mental Health Nursing, Index Nursing College, Malwanchal University, Indore, Madhya Pradesh, India
Pooja Shree Mazumdar Department of Mental Health Nursing, AECS Maaruti College of Nursing, Rajiv Gandhi University of Health Sciences (RGUHS), Bengaluru, Karnataka, India
T. Aruna Department of Obstetrics and Gynecological Nursing, College of Nursing Sciences, Dayananda Sagar University; Chettinad Academy of Research and Education (CARE), Bengaluru, Karnataka/ Chennai, Tamil Nadu, India
Mayanglambam Sankerdev Singh Department of Mental Health Nursing, Madhubani Institute of Nursing and Paramedical Sciences, Madhubani, Bihar, India
Mohammed Umar Department of Nursing, Uttar Pradesh University of Medical Sciences (UPUMS), Saifai, Uttar Pradesh, India
Simon Samuvel S. Department of Psychiatric Nursing, T. S. Misra College of Nursing, T. S. Mishra University, Anora, Lucknow, Uttar Pradesh,
P. Emmanuel Raju Department of Mental Health Nursing, Holy Faith College of Nursing, Dr. N.T.R. University of Health Sciences, Visakhapatnam, Andhra Pradesh, India

DOI:

https://doi.org/10.18203/2320-6012.ijrms20263570

Keywords:

Artificial intelligence, Psychiatry, Machine learning, Deep learning, Precision psychiatry, Mental health, Risk prediction, Personalized mental healthcare, Natural language processing, Digital phenotyping

Abstract

Artificial intelligence (AI) is transforming psychiatric practice by introducing innovative approaches to diagnosis, risk prediction, and personalized mental healthcare. Recent advances in machine learning (ML), deep learning (DL), natural language processing (NLP), digital phenotyping, neuroimaging analytics, and wearable technologies have enhanced the ability to analyze complex clinical, behavioral, and biological data, enabling earlier detection of mental disorders and more accurate prediction of treatment outcomes. AI-assisted clinical decision support systems facilitate the identification of individuals at risk of depression, schizophrenia, bipolar disorder, anxiety disorders, suicide, and relapse while supporting precision psychiatry through individualized treatment recommendations. Furthermore, digital health technologies allow continuous monitoring of patients in real-world settings, promoting timely interventions and improving continuity of care. Despite these promising developments, several challenges remain, including algorithmic bias, limited external validation, lack of explainability, data privacy concerns, ethical issues, and regulatory barriers that may affect clinical implementation. This narrative review synthesizes current evidence regarding the applications of AI in psychiatric diagnosis, predictive analytics, and personalized mental healthcare, while critically discussing technological advances, clinical benefits, implementation challenges, ethical considerations, and future research directions. The review highlights that AI should complement rather than replace psychiatrists, supporting evidence-based, patient-centered, and precision mental healthcare. Continued multidisciplinary collaboration, rigorous validation, transparent AI models, and robust governance frameworks are essential to ensure the safe, equitable, and effective integration of AI into routine psychiatric practice.

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