Advances in artificial intelligence for neuroimaging

Most neuroimaging applications involve a multi-step pipeline encompassing image acquisition, reconstruction, enhancement, registration, segmentation, diagnosis, and prognosis. These steps are fundamental for diagnosing neurological disorders, tracking disease progression, and guiding treatment. Recent advances in artificial intelligence (AI), particularly deep learning, are transforming each stage by improving efficiency, image quality and resolution, accuracy, and clinical utility. This review surveys recent advances and emerging trends across diverse neuroimaging modalities, including multimodal studies that integrate imaging with clinical and molecular data. New AI approaches address long-standing challenges in neuroimaging: physics-informed models incorporate prior knowledge to improve reconstruction, self-supervised learning mitigates the lack of ground-truth data in incomplete datasets, graph neural networks capture the non-Euclidean nature of connectomics, generative diffusion models predict missing contrasts and enable cross modal synthesis, and data harmonization techniques reduce scanner and site variability to improve generalizability. Despite these advances, key barriers such as heterogeneous and biased datasets, limited benchmarking, and regulatory challenges impede the translation of these methods into clinical workflows. We conclude by highlighting priorities for developing reliable, generalizable, and interpretable that can advance both neuroimaging research and real-world patient care.