Clinical Validation and Generalizability of AI and Foundation Models in Neuroimaging

AI tools are increasingly moving from research settings into clinical practice, but their translation remains limited by a lack of robust clinical evidence. Although many models demonstrate promising performance during development, relatively few are rigorously evaluated across independent sites, scanners, acquisition protocols, and patient populations. Important questions remain regarding generalizability, reproducibility, workflow integration, and reliability in real-world clinical environments. This Research Topic focuses on the clinical validation, generalizability, and real-world evaluation of AI and foundation models across a broad range of neuroimaging applications.This Research Topic aims to assess whether AI and foundation models for neuroimaging are sufficiently robust, generalizable, reproducible, and clinically useful for deployment across diverse healthcare settings. We seek studies that evaluate model performance beyond internal development cohorts and provide evidence of real-world applicability across institutions, vendors, populations, and imaging protocols.The Topic focuses on the clinical validation, generalizability, reproducibility, and real-world evaluation of AI and foundation models across diagnostic, prognostic, predictive, and disease-monitoring neuroimaging applications. Technical studies focused solely on model development or performance within a single internal dataset, without meaningful clinical relevance or evidence addressing external validity and generalizability, are outside its primary scope.We welcome articles addressing, but not limited to:• Multi-site and multi-scanner external validation• Generalizability across populations, vendors, field strengths, and imaging protocols• Robustness to distribution shift, domain adaptation, harmonization, and variations in image quality• Clinical utility, diagnostic and prognostic performance, reader studies, and impact on clinical decision-making• Clinical workflow integration, reader studies, and human-AI interaction• Prospective, temporal, and real-world clinical validation• Bias, fairness, and performance across diverse patient populations• Explainability and interpretability of clinically deployed models• Regulatory readiness, reporting standards, and clinical deployment• Reproducibility, calibration, uncertainty quantification, and longitudinal monitoring• Foundation models evaluated in real-world clinical tasks• Prognostic modeling, disease progression prediction, risk stratification, and outcome forecasting• Longitudinal neuroimaging studies and monitoring of disease trajectories• Prediction of treatment response and clinically meaningful outcomes• Validation of imaging biomarkers for diagnosis, prognosis, patient stratification, and precision medicine Topic editors: Anupa A Vijayakumari, Yuncong Ma, Godwin Ogbole Source: https://www.frontiersin.org/research-topics/86228/clinical-validation-and-generalizability-of-ai-and-foundation-models-in-neuroimaging