SynthesisFrontiers in psychiatry2026
Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.
Synthesis in Frontiers in 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.
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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.
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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.
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0 citing papers in PubMed.
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Authors and funding
6 authors.
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Abstract
Background: Schizophrenia (SZ) is a debilitating mental illness that adversely affects social and family interactions, ranking as a leading contributor to global disability. Existing diagnostic approaches, including MRI, PET, and EEG, underscore the necessity for effective predictive strategies to enhance management and reduce costs. Objective: This review evaluates the application of artificial intelligence (AI) methodologies-specifically Machine Learning (ML) and Deep Learning (DL) in predicting SZ using medical imaging data, while addressing existing challenges and identifying key biomarkers to improve diagnostic accuracy. Methods: A systematic literature review was performed using the databases IEEE, PubMed, ScienceDirect, MDPI, Google Scholar, and Springer from inception until March 31, 2026. The initial search generated 820 records, and after a thorough screening process, 185 studies relevant to disease diagnosis, model selection across various neuroimaging modalities, including biomarker identification, were identified. The review protocol has been registered with PROSPORO registration: CRD420251131635. The studies were selected based on different medical imaging data related to SZ. Results: This review presents a thorough examination of advancements in SZ detection via AI methodologies. It highlights not only providing existing predictive techniques, identifies research gaps, biomarkers identification and assessment, and underscores the potential of AI-based ML and DL methods to facilitate early and accurate diagnosis of SZ. Five unimodal and various combinations of multimodal data were examined, along with the AI models' performance metrics from multiple studies. Conclusions: The review provides a comprehensive assessment of AI algorithms relevant to both unimodal and multimodal data, biomarkers of neuroimaging modalities with ROIs, challenges, and limitations of ML and DL models, and future directions of prediction for clinical diagnosis, thereby supporting timely interventions for individuals affected by SZ. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view, identifier CRD420251131635.
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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 OpenQuestion graph.