The integration of artificial intelligence with advanced neuroimaging techniques represents one of the most important developments in contemporary neuroscience and clinical neuroanatomy. This narrative review summarizes how machine learning and deep learning methods can be applied to neuroimaging data to reveal hidden anatomical and functional brain patterns with direct clinical relevance. Neuroanatomy plays a central role in this process, providing the structural framework for interpreting functional, metabolic, and electrophysiological signals. Artificial intelligence-based analysis enables the detection of subtle alterations in gray and white matter, the analysis of brain networks and pathways, and the integration of multimodal imaging data beyond the limits of human perception and conventional statistical approaches. In addition to aiding physicians in establishing the correct diagnosis, these capabilities contribute to earlier and more accurate diagnoses, improved disease differentiation, identification of affected structures within specific health conditions or diseases, and more reliable prognostic assessment. In addition to diagnostic applications, artificial intelligence supports the development of personalized therapeutic strategies, including targeted neuromodulation, neuroimaging-guided surgery, adaptive brain-computer interfaces, and an individualized approach in further disease monitoring. Despite existing challenges related to data heterogeneity, model interpretability, and ethical regulation, the synergy between AI and neuroimaging represents a critical step toward personalized medicine. By leveraging detailed neuroanatomical information, artificial intelligence-driven approaches enable therapies tailored to the unique brain structure and network organization of each individual.
Starčević et al. (Thu,) studied this question.
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