Generation of virtual stains - can AI imitate chemistry?

Histological staining is the foundation of anatomic pathology, yet traditional wet-lab workflows are slow, consume irreplaceable tissue, and are prone to artifactual variability. As precision oncology increasingly demands comprehensive molecular testing from limited biopsy samples, conserving tissue while delivering rapid, accurate diagnoses is an urgent clinical challenge. This review explores "virtual staining" - the digital generation of routine and special stains directly from label-free tissue scans or primary hematoxylin and eosin slides. We examine the shift toward advanced artificial intelligence models that preserve overall tissue architecture, the development of "pathology-aware" quality metrics, and clinical applications like instant multiplexed immunofluorescence and 3D intraoperative biopsies. Ultimately, virtual staining allows pathologists to simultaneously evaluate multiple diagnostic stains without depleting the physical block or waiting for laboratory processing. By adopting rigorous quality controls and optimizing for time-sensitive clinical workflows, this technology is poised to transition from a research novelty into a fundamental tool for preserving invaluable patient tissue and accelerating diagnostic turnaround times.