Emad A Rakha, Jelle Wesseling, Anikó Kovács, Aleš Ryška, Elena Provenzano, Gábor Cserni, Zsuzsanna Varga, Zsuzsanna Bagó-Horváth, Emmanuelle Charafe-Jauffret, Janina Kulka, Carolien H M van Deurzen, Antonio Polónia, Thomas Decker, Paul J van Diest, Cecily Quinn, European Working Group for Breast Screening Pathology (EWGBSP).
Artificial intelligence (AI) is rapidly transforming histopathology, with applications ranging from workflow optimisation and quality assurance to tumour diagnosis, grading, biomarker assessment and estimation of prognosis. While numerous AI algorithms have demonstrated promising analytical and clinical performance, pathology laboratories are increasingly adopting commercially available AI systems with regulatory-approval rather than developing their own algorithms. Existing guidance largely focuses on AI development, validation and regulatory approval, with comparatively little practical direction on the local verification, governance and ongoing assurance required for safe routine clinical implementation. This paper proposes a practical framework for the clinical implementation of AI specifically within pathology laboratories. Rather than addressing AI development, it focuses on the responsibilities of laboratories adopting established AI systems into clinical practice. The framework distinguishes AI applications according to their intended clinical function, recognising that diagnostic applications, biomarker evaluation, workflow optimisation and generative AI applications require different implementation, verification, governance and quality assurance strategies. It further distinguishes algorithm validation, local verification and continuous assurance as complementary stages of implementation and advocates a function-based, risk-proportionate approach integrated within existing laboratory quality management systems. Practical recommendations are provided for workflow integration, interoperability, human oversight, user competency, performance monitoring, incident management, software updates and proportionate re-verification throughout the AI operational lifecycle. By extending implementation beyond regulatory approval, this guidance complements existing AI development and regulatory frameworks rather than replacing them. It provides a practical governance framework for pathology laboratories, professional organisations, accreditation bodies, and healthcare providers to support the safe, standardised, and sustainable integration of AI into routine histopathology while maintaining diagnostic quality, patient safety, and clinical governance.