SynthesisAlpha psychiatry2026
Artificial Intelligence in Schizophrenia Spectrum Disorders: Current Use and Future Perspectives. A Systematic Mapping Literature Review.
Synthesis in Alpha psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) techniques are increasingly applied in psychiatric research, yet their use in schizophrenia spectrum disorders (SSDs) remains heterogeneous. This systematic mapping literature review aimed to examine the current applications of AI systems in SSDs. AI approaches were categorized into natural language processing (NLP), machine learning (ML), and deep learning (DL). Key study characteristics were systematically analyzed, including publication year, type of AI algorithm, study population, clinical outcomes, and instrumental techniques. Trends in journal impact factor (IF) were also evaluated. Methods: A systematic mapping approach was employed to identify and categorize relevant studies investigating AI applications in SSDs. Included studies were analyzed according to predefined evaluation criteria to provide an overview of methodological trends and research focus within the field. Results: A total of 853 studies met the inclusion criteria. Machine Learning emerged as the most frequently utilized AI approach. AI methods were predominantly applied to diagnostic and differential diagnostic tasks in SSDs. The most commonly employed instrumental techniques were magnetic resonance imaging (MRI) and electroencephalography (EEG). Journal Impact Factor varied significantly across study characteristics, with higher IFs observed in studies focusing specifically on schizophrenia, reporting diagnostic outcomes, or employing MRI or EEG-based measures. Conclusions: This review represents the first systematic mapping literature review to comprehensively examine AI applications in schizophrenia spectrum disorders. The findings may support clinicians and researchers in planning, implementing, and evaluating AI-based methodologies in SSD research and clinical contexts. Nevertheless, further studies are needed to establish the clinical validity, robustness, and translational applicability of these algorithms.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.