Practical applications of computational pathology: bridging technology and clinical practice

Computational pathology, driven by advances in artificial intelligence and digital imaging, is rapidly reshaping how pathology data are interpreted and utilised in clinical settings. By leveraging machine learning models and pathology text, tabular, molecular, and imaging data, these technologies offer practical solutions to support routine diagnostics, workflow optimisation, quality assurance, and personalised patient care. From automated screening and triage to diagnostic decision support and prognostic modelling, computational tools are increasingly being evaluated for integration into the pathologist's toolkit. Despite clear potential, real-world adoption remains uneven, and the evidence base is still limited by retrospective study designs, small or enriched cohorts, variable external validation, model generalisability across scanners and institutions, clinical integration, reimbursement, and regulatory authorisation. Addressing these issues requires access to diverse datasets, collaborative model development, analytical and clinical validation using real-world data, robust deployment infrastructure, post-deployment monitoring, and education for end-users. This review explores the current landscape of computational pathology with a focus on actionable clinical applications. It highlights successful use cases, critically appraises common barriers to adoption, and outlines key steps to translate technological advances into clinical value. By bridging the divide between innovation and implementation, computational pathology has the potential to become a vital component of modern diagnostic medicine.