The provided research introduces GigaTIME, a multimodal AI framework designed to transform standard H&E pathology slides into virtual multiplex immunofluorescence (mIF) images. By training on 40 million cells, the model bridges the gap between routine tissue morphology and complex spatial proteomics that are usually too expensive for large-scale use. This technology allowed researchers to create a virtual population of over 14,000 patients, uncovering over a thousand significant associations between protein activations and clinical biomarkers. The study demonstrates that these AI-generated profiles can effectively stratify patients by cancer subtype and predict survival outcomes. Independent validation using TCGA data confirms the model’s reliability and its potential to advance precision immuno-oncology. Ultimately, the sources highlight how computational translation can unlock deep biological insights from widely available, low-cost medical imagery.