Key result
Emerging AI applications using MRI, EEG, and kinesics show promise for improving objective psychiatric diagnosis.
Why the study?
Psychiatrists diagnose psychiatric disorders based on subjective experience rather than pathophysiological insights, creating an urgent need to understand etiology and pathogenesis for better treatments.
This review summarizes the current applications and future directions of artificial intelligence in psychiatric research and diagnosis.
AI applications for psychiatric diagnosis remain investigational; leaves open clinical translation pending prospective validation.
A number of brain research projects have recently been carried out to study the etiology and mechanisms of psychiatric disorders. Although psychiatric disorders are part of the brain sciences, psychiatrists still diagnose them based on subjective experience rather than by gaining insights into the pathophysiology of the diseases. Therefore, it is urgent to have a clear understanding of the etiology and pathogenesis of major psychiatric diseases, which can help in the development of effective treatments and interventions. Artificial intelligence (AI)-based applications are being quickly developed for psychiatric research and diagnosis, but there is no systematic review that summarizes their applications. For this reason, this study briefly reviews three main brain observation techniques used to study psychiatric disorders—namely, magnetic resonance imaging (MRI), electroencephalography (EEG), and kinesics diagnoses—along with related AI applications and algorithms. Finally, we discuss the challenges, opportunities, and future study directions of AI-based applications.
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Liu et al. (2019) conducted a review in Psychiatric disorders. Artificial intelligence applications and algorithms was evaluated. Artificial intelligence applications using MRI, EEG, and kinesics are being rapidly developed to improve the objective diagnosis and pathophysiological understanding of psychiatric disorders.
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