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.

References

Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223-30.

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.

Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9.

Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol. 2018;14:91-118.

Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med. 2019;49(9):1426-48.

Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, et al. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry. 2021;20(2):154-70.

Torous J, Wisniewski H, Liu G, Keshavan M. Mental health mobile phone app usage, concerns, and benefits among psychiatric patients. JMIR Ment Health. 2018;5(4):e11715.

Vaidyam AN, Wisniewski H, Halamka JD, Kashavan MS, Torous J. Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Can J Psychiatry. 2019;64(7):456-64.

Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavioral therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Health. 2017;4(2):e19.

Fiske A, Henningsen P, Buyx A. Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. J Med Internet Res. 2019;21(5):e13216.

Insel TR. Digital phenotyping: technology for a new science of behavior. JAMA. 2017;318(13):1215-6.

Torous J, Onnela JP, Keshavan M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry. 2017;7(3):e1053.

Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. NPJ Digit Med. 2019;2:88.

Gooding P. Mapping the rise of digital mental health technologies: emerging issues for law and society. Int J Law Psychiatry. 2019;67:101498.

Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep. 2019;21(11):116.

Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R, et al. Multimodal machine learning workflows for prediction of psychosis. Mol Psychiatry. 2022;27:997-1006.

Walsh CG, Ribeiro JD, Franklin JC. Predicting suicide attempts in adolescents with machine learning. Clin Psychol Sci. 2018;6(3):457-69.

Bernert RA, Hilberg AM, Melia R, Kim JP, Shah NH, Abnousi F. Artificial intelligence and suicide prevention: a systematic review of machine learning investigations. Int J Environ Res Public Health. 2020;17(16):5929.

Sendak MP, D'Arcy J, Kashyap S, Gao M, Nichols M, Corey K, et al. A path for translation of machine learning products into healthcare delivery. EMJ Innov. 2020;4(1):1-8.

Sun J, Cao R, Zhou M, et al. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr. 2023;88:103746.

Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31-8.

Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, et al. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry. 2009;66(7):700-12.

Abi-Dargham A, Horga G. The search for imaging biomarkers in psychiatric disorders. Nat Med. 2016;22(11):1248-55.

Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019;24(11):1583-98.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.

Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-8.

Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.

Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53.

Topol EJ. Welcoming new guidelines for AI clinical research. Nat Med. 2020;26(9):1318-20.

World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. Available at: https://www.who.int/publications/i/item/9789240029200. Accessed on 02 August 2026.

World Health Organization. Mental health atlas 2020. Geneva: World Health Organization. 2021. Available at: https://www.who.int/publications/i/item/9789240036703. Accessed on 02 August 2026.

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Text Revision (DSM-5-TR). Washington (DC): American Psychiatric Association Publishing. 2022. Available at: https://www.psychiatry.org/psychiatrists/practice/dsm. Accessed on 02 August 2026.

Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273-97.

Breiman L. Random forests. Mach Learn. 2001;45(1):5-32.

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:5998-6008.

Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT 2019. Minneapolis (MN): Association for Computational Linguistics. 2019;4171-86.

Ribeiro MT, Singh S, Guestrin C. "Why should I trust you?": Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016;1135-44.

Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems. 2017;30:4765-74.

Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255-60.

Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, et al. Deep learning-enabled medical computer vision. NPJ Digit Med. 2021;4:5.