Digital pathology and artificial intelligence in neuropathological diagnosis: a comprehensive review of current applications, challenges, and future directions

Abstract Background Digital pathology and artificial intelligence (AI) are fundamentally transforming neuropathological diagnosis by enabling high-throughput, reproducible, and quantitative analysis of complex neural tissue specimens. This comprehensive review examines the current state of AI applications in neuropathological diagnosis, including tumor classification, neurodegenerative disease assessment, and inflammatory condition evaluation, with a focus on recent advancements and methodological limitations. Main body of the abstract A systematic literature search was conducted for studies published between January 2015 and December 2025. Following PRISMA 2020 guidelines, 58 studies were included. AI systems, particularly deep learning models applied to whole-slide images, demonstrated high diagnostic accuracy for brain tumor classification, moderate accuracy for neurodegenerative disease grading, and emerging utility in demyelinating disease assessment. However, significant heterogeneity exists in the methodologies of the included studies. While AI models show promise in automating routine tasks and integrating morphological with molecular data, their performance varies considerably across different neuropathological subfields due to differences in dataset sizes, annotation quality, and the inherent complexity of the diseases. Conclusions Digital pathology combined with AI represents a substantial advancement in neuropathological diagnosis. While current evidence supports the potential of AI systems to achieve high diagnostic accuracy, particularly in neuro-oncology, widespread clinical implementation remains constrained by challenges related to standardization, prospective multi-center validation, and the interpretability of deep learning models.