Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?

Abstract

Schizophrenia (SZ) is a severe mental disorder that exerts a devastating impact on the brain and daily life of patients. It can lead to abnormalities in early brain development and induce a range of symptoms, including hallucinations, thought disorders, decreased motivation, and cognitive impairment. The lifetime prevalence of SZ is approximately 0.7%, which makes it the leading cause of disability-adjusted life years 1 . According to the World Health Organization, approximately 21 million people worldwide are affected by SZ 2 . The average age of onset is 18 years for women and 25 years for men 3 , 4 . SZ has emerged as a significant global public health concern that demands urgent attention 5 . Currently, the diagnosis of and treatment strategies for SZ primarily rely on symptomatology research. Despite the use of the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) to diagnose SZ, clinicians encounter numerous challenges. For example, SZ poses a major challenge in terms of accurate diagnosis and individualized treatment because of its marked heterogeneity in clinical manifestations, symptom profiles, and disease trajectories. This inherent complexity necessitates a considerable reliance on the clinical expertise and professional discernment of psychiatrists throughout the diagnostic and therapeutic processes. However, the excessive dependence on incomplete clinical impression and noncomprehensive patient evaluation potentially compromises the accuracy of diagnosis and efficacy of treatment interventions. Epidemiological studies have indicated that the misdiagnosis rate of SZ in clinical practice can be as high as 25% 6 . Approximately 30% of patients with SZ exhibit treatment-resistant characteristics and respond poorly to conventional antipsychotic medications, indicating a major challenge 7 . Furthermore, patients considered as having treatment-resistant SZ (TRS) typically exhibit significant pharmacological resistance, indicating the inability of the existing medications to adequately alleviate positive or negative symptoms. Despite the lack of efficacy of existing medications, these patients frequently endure severe side effects from pharmacotherapy during treatment 8 . This situation highlights the limitations of the current diagnostic and therapeutic approaches for SZ. Furthermore, it underscores the urgent need to address the scientific challenge of deconstructing the high heterogeneity of the disorder to achieve individualized and precise diagnostic and treatment strategies. There is a growing consensus that new artificial intelligence (AI) methodologies are essential for the advancement of SZ research. The human brain functions similarly to digital systems, whereas machines operate digitally. AI enables machines to learn and identify patterns and relationships from large, representative datasets. By integrating complex, heterogeneous, and multidimensional data through AI algorithms, AI serves as a revolutionary tool in psychiatric research and precision medicine. Consequently, the development of diagnostic and individualized precision treatment methods based on objective biological markers holds significant clinical importance.

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