A sector review on AI in digital pathology outlined recent innovations, validation challenges, and integration barriers that laboratories face when deploying machine‑learning tools for diagnostics. The authors discussed algorithmic performance gains in tumor detection and quantitation, while flagging reproducibility, regulatory validation, workflow integration, and data‑sharing as persistent obstacles to broad clinical adoption. The paper recommends structured validation frameworks, cross‑site benchmarking, and multidisciplinary governance to accelerate safe deployment. Hospital labs, AI vendors, and regulators should coordinate on standardized datasets and prospective validation studies to move algorithms from research into routine diagnostics.