Digital pathology is evolving from an adjunct technology to a core diagnostic platform in the United States, with a narrative review published on June 27 outlining a strategic framework for its implementation and sustainability. The review highlights that while clinical adoption of digital pathology is accelerating, laboratories continue to face significant barriers such as high capital and operating costs, workflow disruptions, interoperability challenges, and complex regulatory and reimbursement requirements.
The authors conducted a targeted narrative review using PubMed/MEDLINE and Google Scholar to identify English-language publications from January 1, 2014, through December 31, 2025. Their research focused on key topics including whole slide imaging, image management systems, laboratory information system integration, validation processes, reimbursement policies, U.S. Food and Drug Administration clearance procedures, Clinical Laboratory Improvement Amendments oversight, College of American Pathologists accreditation standards, interoperability protocols, cybersecurity measures, and artificial intelligence applications. Additional information was gathered from regulatory organizations’ guidance documents and public databases.
According to the article's findings, successful digital pathology programs require attention to several domains: foundational infrastructure such as scanners and networking; workflow redesign across pre-analytic to post-analytic phases; robust validation and quality management systems; compliance with regulatory standards; strategies for cost capture; plans for interoperability; strong cybersecurity controls; education initiatives; change management processes; ongoing governance structures; and long-term performance monitoring.
A central element described is an institution-level artificial intelligence readiness model. This model assesses data quality standards, integration capabilities with existing systems, validation methods for new technologies or algorithms, continuous monitoring practices for AI tools in use, governance frameworks guiding responsible deployment of AI solutions in diagnostics settings, and workforce competencies needed to support these changes safely.
The review concludes that implementing digital pathology requires more than acquiring scanners or expanding data storage capacity. It calls for a comprehensive lifecycle-oriented approach integrating infrastructure development with workflow innovation—while maintaining rigorous validation protocols—and ensuring institutional readiness for future advances like artificial intelligence.
