Computational pathology predictions are a breakthrough approach in pathology using advanced algorithms and machine-learning techniques to increase diagnosis accuracy and efficiency. These computer methods use vast histological data to produce reliable, consistent results above accepted manual interpretations. For instance, Lee et al. have demonstrated that using computational techniques to analyse the surrounding non-cancerous tissue in prostate cancer can yield important information on the chance of cancer returning ( ). In oral malignancies and early-stage oestrogen receptor-positive breast tumours, Lu et al. showed a strong association between survival rates and characteristics defining the shape ( ) and orientation of the nucleus ( ). Such a combination of computational prediction in pathology reduces human error and accelerates the diagnosis process, thereby improving patient outcomes. The continuous evolution of computational pathology has significant power to change tailored treatment and enable the discovery of fresh biomarkers. The combination of technology and pathology (Figure ) can transform healthcare by improving diagnosis accuracy, allowing targeted therapies, and raising the general quality of medical treatment. The graphic shows a thorough, pathologist-centered medical system combining many data sources for improved diagnosis and patient care. Aggregated into the Laboratory Information System (LIS), clinical data from electronic health records (EHR), omics data from molecular pathology, whole slide images (WSIs) from digital pathology, and clinical laboratory results are included. This integration is the foundation of "Algorithm 1," a diagnostic tool synthesising several kinds of data to raise diagnostic accuracy. Updated disease-related data gathered during patient follow-up over time is merged with the original dataset to develop "Algorithm 2." Through constant improvement of diagnostic and prognostic capacities made possible by this iterative process, more exact and individualised patient treatment finally results ( ).