Histopathological analysis traditionally relies on Hematoxylin and eosin (H&E) staining. However, comprehensive differential diagnoses often require additional histochemical stains, leading to increased diagnostic time and costs. To address these limitations, we introduce AI-XStainer, a novel virtual staining tool powered by advanced deep learning techniques. This system adeptly transforms conventional H&E-stained images into multiple histochemical visualizations, marking a significant advancement in diagnostic histopathology. To validate AI-XStainer's efficacy, we conducted a rigorous clinical trial involving seasoned nephropathologists. Using the OmniST dataset—a carefully curated collection of 1646 whole slide images (WSIs) representing diverse patient samples, including renal transplant, liver explants, non-malignant renal disease, and Helicobacter pylori gastritis and paired with standard stains such as Masson’s trichrome (MT), Periodic acid-Schiff (PAS), Jones methenamine silver (JMS), and Toluidine blue (TB)—our tool underwent intensive clinical evaluation. The results were precise: our virtually stained slides enabled board-certified experienced nephropathologists (>10) to achieve diagnostic accuracy on par with, if not superior to, traditional staining techniques. AI-XStainer consistently outperformed various assessment benchmarks, including patch-level and slide-level visual Turing Tests, and showed favorable performance in the Frechet Inception Distance comparison, further underscoring its transformative potential. In summary, AI-XStainer emerges as a promising and paradigm-shifting solution for expeditious and precise histopathological diagnosis with significant potential clinical impact.