Research groups
Dominic Oliver
PhD
Senior Researcher
I am a Senior Researcher working with Professor Philip McGuire. My interests lie in prevention of psychosis and other severe mental disorders, particularly in combining prediction modelling methods, artificial intelligence and routinely collected healthcare data to improve identification of individuals at risk, predict their clinical outcomes and their response to interventions.
I lead the Wellcome Trust-funded ARIADNE (ARtificial Intelligence-based Assessment to Detect iNdividuals with Emerging psychosis risk) programme, which aims to transform the detection of individuals at clinical high risk for psychosis. This work aims to develop and validate a novel voice-based assessment platform powered by generative artificial intelligence that can conduct naturalistic clinical interviews to identify psychosis risk.
I also lead biomarker acquisition and analysis within the Stratification and Treatment in Early Psychosis (STEP) programme, an international series of multicentre clinical trials investigating cannabidiol (CBD) across different stages of psychosis, including clinical high risk for psychosis, first-episode psychosis, and treatment-resistant psychosis.
I completed my PhD in 2022 ("Improving prediction of psychosis risk") at the Institute of Psychiatry, Psychology & Neuroscience, King's College London, under the supervision of Professor Paolo Fusar-Poli and Professor Philip McGuire. Alongside my work at the University of Oxford, I maintain active collaborations with colleagues at King’s College London and other international research centres, with the aim of advancing precision approaches to the prediction and prevention of severe mental illness.
Key publications
Joint detection of risk for psychotic disorders or bipolar disorders in clinical practice in the UK: development and validation of a clinical prediction model.
Journal article
Arribas M. et al, (2026), Lancet Psychiatry, 13, 14 - 23
Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records.
Journal article
Arribas M. et al, (2025), Mol Psychiatry, 30, 2931 - 2942
Using Electronic Health Records to Facilitate Precision Psychiatry.
Journal article
Oliver D. et al, (2024), Biol Psychiatry, 96, 532 - 542
Exploring causal mechanisms of psychosis risk.
Journal article
Oliver D. et al, (2024), Neurosci Biobehav Rev, 162
Real-world implementation of precision psychiatry: Transdiagnostic risk calculator for the automatic detection of individuals at-risk of psychosis.
Journal article
Oliver D. et al, (2021), Schizophr Res, 227, 52 - 60
Recent publications
The subjective effects of △9-tetrahydrocannabinol: A systematic review and dose-response meta-regression.
Journal article
Goodwin I. et al, (2026), Neurosci Biobehav Rev
Predicting long-term poor outcomes in individuals at clinical high risk for psychosis using real-world clinical data: the OASIS1000 prospective study.
Journal article
Logeswaran Y. et al, (2026), World Psychiatry, 25, 295 - 306
Real-world comprehensive care of people living with schizophrenia: recommendations across different settings and clinical stages.
Journal article
Fusar-Poli P. et al, (2026), World Psychiatry, 25, 231 - 264
Child and adolescent psychiatry: challenges, solutions, opportunities, and future directions.
Journal article
Cortese S. et al, (2026), World Psychiatry, 25, 190 - 224
Development and validation of a precision treatment rules for first-line antipsychotic recommendations in first episode psychosis jointly incorporating effectiveness, side effects and patient preferences.
Journal article
Krakowski K. et al, (2026), Transl Psychiatry
