Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data
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…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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