Core Viewpoint - The integration of multimodal neuroimaging and artificial intelligence (AI) is revolutionizing the early diagnosis and personalized treatment of neuropsychiatric disorders, addressing the challenges posed by their complex pathology and clinical heterogeneity [2][6]. Multimodal Neuroimaging: A Comprehensive Brain Examination - Traditional single-modality brain examinations are limited, while multimodal imaging can decode the brain from structural, functional, and molecular dimensions, enabling early intervention [7][8]. - Structural imaging (e.g., MRI) reveals brain tissue volume and cortical thickness, functional imaging (e.g., fMRI, EEG) captures neuronal activity, and molecular imaging (e.g., PET) tracks pathological markers like amyloid proteins, providing early warnings for conditions like Alzheimer's disease [9]. AI as a Puzzle Solver - AI demonstrates three key capabilities in handling vast heterogeneous data: feature fusion (early, mid, and late fusion), deep learning models, and clinical prediction tools, significantly enhancing diagnostic accuracy [12][13]. - For instance, multimodal AI models have improved early Alzheimer's diagnosis accuracy to 92.7%, surpassing single-modality methods by over 15% [13]. Practical Achievements: AI's Impact - AI has achieved high diagnostic accuracy, distinguishing Alzheimer's from Lewy body dementia at 87% and predicting epilepsy seizures with over 98% accuracy [14]. - It can predict the efficacy of depression medications with 89% accuracy and assess cognitive decline rates [15]. - AI identified three subtypes in over 2,000 bipolar disorder patients, guiding personalized treatment approaches [16]. Challenges and Breakthroughs: Path to Clinical Application - The integration of multimodal neuroimaging data faces challenges such as data availability, heterogeneity, and AI model interpretability, compounded by issues like class imbalance, algorithm bias, and data privacy [20]. - Addressing these challenges is crucial for developing robust AI models based on multimodal neuroimaging [20]. Future Research Directions - The future of AI in neuropsychiatric disorders includes the development of transformer models for cross-modal data processing, dynamic monitoring of brain network changes, and creating lightweight models for clinical use [23][24]. - Despite significant advancements, further exploration of clinical effectiveness and usability is needed to transition from research to practical applications [24].