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Artificial intelligence-enabled predictive modelling in psychiatry: overview of machine learning applications in mental health research

Published online by Cambridge University Press:  22 August 2025

Gemma Lewin Open the ORCID record for Gemma Lewin [Opens in a new window] ,
Emeka Abakasanga ,
Isabel Titcombe ,
Georgina Cosma  and
Satheesh Gangadharan Open the ORCID record for Satheesh Gangadharan [Opens in a new window]
Gemma Lewin
Affiliation:
A Specialty Trainee (Year 5) in the psychiatry of intellectual disability, working in Leicestershire Partnership Trust, Leicester, UK. She has an interest in the physical health needs of individuals with intellectual disability and is currently undertaking research and a postgraduate certificate in this field.
Emeka Abakasanga
Affiliation:
A research associate in artificial intelligence in the Department of Computer Science at Loughborough University, UK. He holds a PhD in electrical and computer engineering from the Ben-Gurion University of the Negev, Israel. His research focuses on information theory, artificial intelligence for healthcare and industrial applications, signal processing and data science.
Isabel Titcombe
Affiliation:
An undergraduate student at the University of Leicester, UK. She is studying psychology with cognitive neuroscience, graduating in 2025. She aspires to become a clinical psychologist in the future.
Georgina Cosma
Affiliation:
A professor of artificial intelligence and data science in the Department of Computer Science at Loughborough University, UK. She holds a PhD in computer science from the University of Warwick, UK. Her research focuses on artificial intelligence in healthcare, and on neural information processing, modelling and retrieval.
Satheesh Gangadharan*
Affiliation:
A consultant in the psychiatry of intellectual disability, working in Leicestershire Partnership NHS Trust, Leicester, UK. He is also a clinical researcher with interests in intellectual disability, autism and use of artificial intelligence in healthcare. One of his current research projects is focused on the use of machine learning in identification of the clusters and trajectory of multiple long-term conditions in people with intellectual disability.
*
Correspondence Satheesh Gangadharan. Email: s.gangadharan1@nhs.net
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Summary

Machine learning, an artificial intelligence (AI) approach, provides scope for developing predictive modelling in mental health. The ability of machine learning algorithms to analyse vast amounts of data and make predictions about the onset or course of mental health problems makes this approach a valuable tool in mental health research of the future. The right use of this approach could improve personalisation and precision of medical and non-medical treatment approaches. However, ensuring the availability of large, good-quality data-sets that represent the diversity of the population, along with the need for openness and transparency of the AI approaches, are some of the challenges that need to be overcome. This article provides an overview of current machine learning applications in mental health research, synthesising literature identified through targeted searches of key databases and expert knowledge to examine research developments and emerging applications of AI-enabled predictive modelling in psychiatry. The article appraises both the potential applications and current challenges of AI-based predictive modelling in psychiatric practice and research.

Keywords

Machine learningpredictive modelmental healthartificial intelligencepsychiatry

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Type
Article
Information
BJPsych Advances , First View , pp. 1 - 7
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal College of Psychiatrists

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