The Cedars-Sinai-led Path2Space model, published in Cell, uses deep learning on routine H&E biopsy slides to infer spatial gene expression across thousands of loci in breast tumors, slashing profiling time from weeks to minutes and cost from thousands of dollars to near-zero marginal expense. This observational computational study trained on one large breast cancer cohort and validated across three independent retrospective datasets, achieving solid concordance for ~5,000 genes; however, it is not an RCT and lacks the randomization or prospective endpoints needed to establish clinical utility. Prior spatial transcriptomics work, such as the 10x Genomics Visium platform studies in Nature Biotechnology (2021), highlighted how high costs limited analyses to cohorts under 50 patients, exactly the bottleneck Path2Space claims to solve. Yet the original coverage underplays generalization limits: performance was breast-cancer-specific, resolution remains at 10-20 cell patches rather than single-cell, and no external multi-ethnic validation sets were reported, raising risks of biased biomarker discovery. Related economic analyses from ASCO (2023) document diagnostic costs rising 8-12% annually, lending credence to accessibility gains, but Path2Space's spatial pattern biomarkers for treatment response still require head-to-head trials against standard assays like Oncotype DX. Conflicts of interest include senior author affiliations with both Cedars-Sinai and the NCI, though the paper discloses standard funding sources. If validated, this tool could democratize precision oncology; without it, enthusiasm risks outpacing evidence.