Pathology AI clinical integration: From promise to practice

Despite substantial advances in digital and computational pathology over the past decade, pragmatic and systematic guidance for translating AI models from research into clinical practice remains limited. Most existing frameworks offer only high-level recommendations on validation, regulatory compliance, and implementation, often lacking pathology-specific roadmaps aligned with laboratory standards. This review first summarizes current progress in digitalization and AI in pathology, with emphasis on key applications in biomarker quantification, molecular status prediction, and multimodality integration. We then outline nine major components essential for pathology AI clinical integration, from defining clinical needs through analytical and clinical validation, workflow interoperability, to health economics, and further summarize and compare eight available frameworks. Next, we introduce the AI Development and Optimization in Pathology for Translation (ADOPT) framework, a practical, phased deployment roadmap specifically designed to align with College of American Pathologists and Clinical Laboratory Improvement Amendments (CAP/CLIA) guidelines, to facilitate safe and effective clinical integration of pathology AI tools. We conclude by discussing future perspectives of digital and computational pathology in cancer care, emphasizing widespread clinical adoption of digitized pathology, effective multidisciplinary collaboration, and optimized pathologist-AI synergy to fully realize the promise of pathology AI.