Artificial intelligence and virtual reality applied to the clinical care of women with schizophrenia: A systematic review.
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".