MACHINE LEARNING-DRIVEN COGNITIVE ASSESSMENT IN PSYCHIATRIC DISORDERS: CURRENT ADVANCES AND FUTURE PERSPECTIVES-A REVIEW

Abstract

Psychiatric disorders are among the leading causes of disability worldwide and are frequently associated with substantial cognitive dysfunction that significantly impairs daily functioning, social interaction, occupational productivity, and overall quality of life. Cognitive deficits in psychiatric illnesses, including schizophrenia, major depressive disorder, bipolar disorder, anxiety disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder, often remain underdiagnosed or inadequately managed due to the subjective nature of conventional assessment techniques. Recent developments in artificial intelligence, particularly machine learning (ML), have introduced transformative opportunities for objective, scalable, and personalized cognitive assessment in psychiatric care. Machine learning-driven cognitive assessment integrates clinical, neuroimaging, electrophysiological, behavioral, and digital phenotyping data to identify subtle cognitive impairments, predict disease progression, and support clinical decision-making. Advanced computational approaches such as supervised learning, unsupervised learning, deep learning, and natural language processing have demonstrated considerable utility in classifying psychiatric conditions, predicting treatment responses, and enhancing diagnostic precision. Furthermore, neuroimaging modalities, electroencephalography, smartphone-based behavioral analytics, and wearable technologies increasingly contribute to precision psychiatry by enabling continuous monitoring of cognitive performance. Despite promising advancements, several challenges remain, including data heterogeneity, algorithmic bias, limited interpretability, privacy concerns, inadequate clinical validation, and ethical issues surrounding patient confidentiality. Therefore, integrating explainable artificial intelligence and standardized datasets is essential for achieving clinical translation. This review comprehensively explores the current landscape of machine learning-driven cognitive assessment in psychiatric disorders, highlighting technological advances, clinical applications, limitations, ethical implications, and future directions toward personalized psychiatric care and precision mental health interventions.