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Artificial intelligence and virtual reality applied to the clinical care of women with schizophrenia: A systematic review.

Published online by Cambridge University Press:  27 August 2024

J. P. Paolini San Miguel ,
M. Natividad ,
M. V. Seeman ,
B. Palacios ,
A. Balagué ,
E. Román ,
N. Bagué ,
E. Izquierdo ,
H. Cachinero  and
J. A. Monreal
...Show all authors
J. P. Paolini San Miguel*
Affiliation:
1Mental Health, Mutua Terrassa University Hospital. University of Barcelona, Terrassa, Spain
M. Natividad
Affiliation:
1Mental Health, Mutua Terrassa University Hospital. University of Barcelona, Terrassa, Spain
M. V. Seeman
Affiliation:
2Psychiatry, University of Toronto, Toronto, Canada
B. Palacios
Affiliation:
3Clinical and Health Psychology, Autonomous University of the State of Morelos, Morelos, Mexico
A. Balagué
Affiliation:
1Mental Health, Mutua Terrassa University Hospital. University of Barcelona, Terrassa, Spain
E. Román
Affiliation:
1Mental Health, Mutua Terrassa University Hospital. University of Barcelona, Terrassa, Spain
N. Bagué
Affiliation:
1Mental Health, Mutua Terrassa University Hospital. University of Barcelona, Terrassa, Spain
E. Izquierdo
Affiliation:
1Mental Health, Mutua Terrassa University Hospital. University of Barcelona, Terrassa, Spain
H. Cachinero
Affiliation:
1Mental Health, Mutua Terrassa University Hospital. University of Barcelona, Terrassa, Spain
J. A. Monreal
Affiliation:
1Mental Health, Mutua Terrassa University Hospital. University of Barcelona, Terrassa, Spain
A. González Rodríguez
Affiliation:
1Mental Health, Mutua Terrassa University Hospital. University of Barcelona, Terrassa, Spain
*
*Corresponding author.

Abstract

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Introduction

Artificial intelligence (AI) and virtual reality (VR) are useful tools that can improve precision medicine and can prove useful in the clinical care of patients with psychosis.

Objectives

Our aim was to determine whether AI and VR have been applied to the prediction of clinical response in women with schizophrenia.

Methods

A systematic review was carried out in PubMed and Scopus from inception to September 2023 by using the PRISMA guidelines. Search terms: (“artificial intelligence” OR “intelligent support” OR “machine intelligence” OR “machine learning” OR “virtual reality” OR “intelligent agent” OR “neural networks” OR “virtual reality” OR “digital twins”) AND (“schizophrenia” OR “psychosis”) AND (“women” OR gender”). Inclusion criteria: 1)English, French, German or Spanish language, 2) reporting treatment response in schizophrenia (as long as information in women was included), and 3) including AI and VR techniques.

Results

From a total of 320 abstracts initially screened (PubMed:182, Scopus:138), we selected 6 studies that met criteria.

  • - Prediction of treatment response. (1) Clinical information, genetic risk score and proxy methylation score have been shown to improve prediction models. (2) Graph-theory-based measures have been combined with machine learning.

  • - Therapeutic drug monitoring. (1) A machine learning model has been useful in predicting quetiapine blood concentrations.

  • - Pharmacovigilance. (1) Machine learning has connected prolactin levels and response in olanzapine-treated patients. (Zhu et al., 2022).

  • - Treatment-resistant schizophrenia (TRS). (1) Women with TRS have been found to receive clozapine less frequently than men (adjusted for sociodemographic, biological and clinical factors). (2) Statistical learning approach: Women have been found to respond better to clozapine than men.

Conclusions

AI, including machine learning, show promising results in the prediction of treatment response in women with schizophrenia. As of yet, digital twins have not been investigated to test specific interventions or to personalize treatment in women with schizophrenia.

Disclosure of Interest

None Declared

Type
Abstract
Information
European Psychiatry , Volume 67 , Special Issue S1: Abstracts of the 32nd European Congress of Psychiatry , April 2024 , pp. S734 - S735
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of European Psychiatric Association
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