High Time for Pathology AI: Practical Considerations for Turning Potential into Practice

Artificial intelligence (AI) has the potential to improve diagnostic accuracy, reproducibility, and efficiency in anatomical pathology, but technical success alone does not establish clinical utility. In this Perspective, we examine practical requirements for translating computational pathology tools into routine practice, including prioritization of genuine clinical bottlenecks, representative datasets, independent validation, workflow integration, costs, clinician oversight, regulatory compliance, and post-deployment monitoring. We distinguish evidence from retrospective model evaluations, controlled reader studies, prospective workflow assessments, and economic simulations, recognizing that their conclusions are not interchangeable. We also propose a staged, decision-oriented framework through which laboratories can assess clinical need, select technologies, perform local validation, conduct prospective pilots, deploy with safeguards, and monitor performance throughout the AI lifecycle. Evidence from selected applications is encouraging, but effectiveness, cost-effectiveness, and readiness for routine use vary by intended purpose and practice environment. Responsible adoption should therefore be guided by demonstrated clinical benefit, operational feasibility, and ongoing evaluation rather than algorithmic performance alone.