A Review for Bridging Clinical and Technical Gaps with Hybrid ML in Schizophrenia Diagnosis

Abstract

Schizophrenia is a complex psychiatric disorder in which traditional diagnostic methods, relying on subjective clinical assessment, suffer from significant limitations in scalability and early intervention. Machine learning can bring a paradigm shift to objective, data-driven diagnosis, but a critical gap exists between its technical performance and real-world clinical translation. The review systematically analyzes and synthesizes findings from 30 seminal studies on ML applications in schizophrenia diagnosis within the period of 2018–2026, following a structured methodology to evaluate models, datasets, performance, and translational challenges. Our review indicates that, though high accuracies (82–96%) have been reported using conventional and deep learning models in controlled research settings, essential barriers critically restrain their clinical utility: heavy class imbalance, a lack of model interpretability, that is, the “black-box” problem, biased datasets, and high computational costs. These limitations diminish their diagnostic accuracy in the real world and clinician trust. Hybrid ML frameworks are available with integrated explainable AI for transparency, GANs for data augmentation, and federated learning for privacy-preserving collaboration in order to bridge these gaps. The road to equitable precision psychiatry will have to be charted by overcoming socio-technical barriers through interdisciplinary co-design and adherence to emerging global ethical AI standards, for example, IEEE P7000, developing lightweight, accessible tools. This review provides a strategic roadmap to transition ML from a research tool into a clinically viable, equitable, and trustworthy asset for global mental health, with the ultimate aim of reducing misdiagnosis and improving patient outcomes.

Received: 22 November 2025 | Revised: 3 April 2026 | Accepted: 25 May 2026

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Syed Mossabbir Hossain: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft. Nitun Kumar Podder: Conceptualization, Software, Validation, Resources, Writing – review & editing, Supervision, Project administration. Md. Raihanul Haque: Formal analysis, Data curation, Visualization. Poly Akter: Formal analysis, Investigation. Tasfia Rahman Asma: Formal analysis, Investigation.