Artificial Intelligence in Neuroimaging: Multicenter Performance, Clinical Outcomes, and Ethical Considerations
Shreya Sankar, Khyber A. Rabbi, Kenze Abdelkhalek, Abdulaziz Alharbi, Elochukwu Okafor, Khushi Singla, Pabodha P. Karunanayaka, Prathit SharmaABSTRACT
Background
Artificial intelligence (AI) and machine learning (ML) have revolutionized neuroimaging, providing novel tools for diagnostic accuracy, workflow optimization, and predictive modeling. Recent advancements in deep learning architectures, including convolutional neural networks (CNNs), U‐Net variants, and foundation models, have achieved remarkable performance in lesion detection, segmentation, and prognostication. However, challenges in multicenter validation, ethical governance, and equitable generalizability remain barriers to widespread clinical adoption.
Methods
A targeted narrative review was conducted via PubMed and Google Scholar (2015–2025). Studies were selectively included based on their emphasis on multicenter data, external validation across institutions, or federated learning frameworks to prioritize evidence of cross‐institutional generalizability over an exhaustive catalogue of all AI applications.
Results
AI algorithms demonstrated high diagnostic accuracy across modalities, with CNN‐based models achieving Dice similarity coefficients of 0.79–0.90 for brain tumor segmentation and area‐under‐curve (AUC) values exceeding 0.98 in intracranial hemorrhage detection. Federated learning approaches improved generalizability without compromising data privacy. However, concerns include data heterogeneity, limited interpretability, and lack of standardized validation frameworks.
Ethical Considerations
Key ethical challenges involve data privacy, algorithmic bias, and management of incidental findings. Current regulatory frameworks, including GDPR and HIPAA, inadequately address AI‐specific issues, necessitating the development of adaptive, transparent, and globally harmonized governance systems.
Conclusion
AI in neuroimaging holds substantial promise for enhancing diagnostic precision and clinical efficiency. To realize its full potential, multicenter collaboration, explainable AI, and robust ethical oversight are essential to ensure equitable and trustworthy integration into clinical practice.