Yongzheng Hu, Yabin Wang, Xinyue Qi, Wei Jiang
Kidney diseases remain heterogeneous and mechanistically complex, and current experimental models only partially capture patient-specific dynamics. We advance artificial-intelligence virtual cells (AIVCs) as a translational framework that learns cross-modal representations of renal cell states, performs high-throughput in silico perturbations, and improves via experiment-in-the-loop updates with organoid and real-world readouts. We outline a methodological pathway-data, representation, intervention, evaluation, deployment-anchored to atlas-grade tissue references (e.g., Kidney Precision Medicine Project) and complementary Human Cell Atlas resources, paired molecular-clinical cohorts, and minimally invasive urinary modalities to enable longitudinal, patient-centered modeling. AIVCs complement physical cells: molecular signals are harmonized into uncertainty-calibrated latent states; counterfactual simulations explore dose-time-context responses; and multicellular interactions are formalized as testable niche dynamics. To address competence and trust, we adopt a risk-proportionate verification-validation-uncertainty perspective aligned with contemporary guidance, emphasizing multisite external validation, counterfactual validity on held-out perturbations, probabilistic calibration with decision utility, and subgroup fairness auditing. Clinically, we map opportunities across mechanism reconstruction, individualized treatment-response prediction, nephrotoxicity triage, trial emulation, and routes toward kidney digital twins. Finally, we propose a practical AIVC-organoid partnership-simulate → experiment → validate → iterate-to prioritize hypotheses, shrink the experimental search space, and link mechanistic evidence to bedside decisions. By integrating representation learning, virtual experimentation, and continual updating with interoperable data standards and governance, AIVCs offer an actionable roadmap for precision nephrology.