A team in China has developed RIDGE, an AI system that predicts cancer molecular biomarkers directly from routine hematoxylin and eosin (H&E) pathology slides. The approach aims to translate widely available clinical tissue workflows into biomarker-ready information without requiring separate molecular assays for every patient. The paper highlights performance across tumor contexts by extracting biomarker-relevant signals embedded in standard histology images. For oncology teams, this could reduce turnaround time and support faster treatment selection when biomarker testing capacity is constrained. Regulatory and validation requirements remain the key hurdle, but the work strengthens the case for AI-assisted pathology as an operational layer in precision medicine.