How Artificial Intelligence is Important for Brain Mapping: A Recent Research Review

The integration of artificial intelligence (AI) into brain mapping has become increasingly pivotal, effectively translating complex neuroimaging data into clinically relevant insights for neurological and psychiatric disorders. This narrative review encompasses recent literature published between January 2015 and August 2026. It analyzes approximately 28 primary studies identified through a systematic search of databases including PubMed, Scopus, Web of Science, and IEEE Xplore. It seeks to examine the application of various AI methodologies, including convolutional neural networks, recurrent architectures, autoencoders, and graph neural networks, in both structural and functional neuroimaging. The discussion emphasizes the growing role of AI in diverse areas, such as the multimodal integration of MRI, fMRI, PET, and CT imaging; connectomics and functional-structural network mapping; and the classification of disorders, including Alzheimer's disease, Parkinson's disease, and schizophrenia. Additionally, it considers applications in preoperative planning and predicting neurostimulation responses. The review also highlights the significance of explainable AI techniques, such as saliency maps and GradCAM, as well as innovative strategies, including generative models for image synthesis and augmentation. Emerging themes, including foundation models and neuromorphic computing, are further explored. Nonetheless, significant challenges remain, including data scarcity, limited generalizability, limited interpretability, and the extensive computational demands of whole-brain connectomics. Ethical and regulatory considerations, particularly regarding data privacy, algorithmic fairness, and equitable representation, are vital for the responsible use of AI in clinical environments. Ultimately, the clinical efficacy of AI depends not only on technical performance but also on thorough validation, interdisciplinary collaboration, and governance measures that prioritize the needs of clinicians and patients in decisionmaking processes.