Schizophrenia (SZ) is a severe mental disorder that impacts brain function and daily life, with a global prevalence of 0.7% and an average age of onset of 18 for women and 25 for men. Current diagnostic methods, which rely on symptom-based evaluations, often result in misdiagnosis, with rates as high as 25%. About 30% of patients experience treatment-resistant symptoms, indicating the limitations of current therapies. Artificial intelligence (AI), particularly machine learning and deep learning, offers potential solutions by analyzing complex data sets and identifying subtle biological markers, improving diagnosis and enabling more personalized treatment strategies.

AI’s role in SZ extends beyond diagnosis to personalized treatment planning, prediction of treatment responses, and medication management. By analyzing neuroimaging, cognitive, and genetic data, AI has helped predict disease progression, identify high-risk individuals, and tailor interventions. AI also shows promise in drug development, reducing the time and costs of discovering new treatments for SZ. Despite these advancements, challenges such as data privacy concerns, validation of AI models, and ensuring transparency remain. Moving forward, AI is expected to complement clinicians, enhancing personalized care and improving patient outcomes, but it should remain a supportive tool rather than replace clinical expertise.

Reference: Jiang S, Jia Q, Peng Z, et al. Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia? Schizophrenia (Heidelb). 2025;11(1):32. doi: 10.1038/s41537-025-00583-4.