Background and purpose
In the United States, digital pathology (DP) is moving from an adjunct technology to an enterprise diagnostic platform. Despite accelerating clinical adoption, many laboratories face persistent barriers such as high capital and operating costs, workflow disruptions, interoperability challenges, and complex regulatory and reimbursement environments. This narrative review focuses on defining and operationalizing an organization’s artificial intelligence (AI) readiness for safe and sustainable adoption and proposes a practical lifecycle framework for implementing and sustaining a DP program.
method
We performed a targeted narrative review based on PubMed/MEDLINE and Google Scholar searches for English-language publications from January 1, 2014 to December 31, 2025. Core search concepts included DP, whole-slide imaging, image management/display systems, laboratory information system integration, validation, reimbursement, U.S. Food and Drug Administration clearance, clinical laboratory improvement modification oversight, College of American Pathologists accreditation, and interoperability. Standards, Cybersecurity and AI. We complemented our database searches with key guidance from U.S. regulatory and professional organizations and reference screening and review of public databases. We prioritized peer-reviewed literature and used web-based regulatory sources when they were reliable primary references. We also brought in our professional experience and knowledge in DP and AI.
result
Key implementation domains span underlying infrastructure (scanners, storage/networking, unified image management platform), workflow redesign across pre-analysis, analysis, and post-analysis phases, validation and quality control, regulatory compliance and certification, cost understanding, interoperability strategy, cybersecurity and access control, education and change management, and long-term governance. We also describe an institution-level AI readiness model that can be assessed across data quality, integration, validation, monitoring, governance, and workforce capabilities to support safe clinical AI implementation.
conclusion
Digital pathology implementation extends beyond scanners and data storage. Lasting success requires a comprehensive lifecycle-oriented framework that integrates infrastructure, workflow redesign, validation, interoperability, AI-enablement, security, education, performance monitoring, and organizational governance. By proactively addressing these areas and clearly distinguishing between device certification and laboratory validation and operational quality control, pathology departments can realize the clinical and operational benefits of digital pathology while positioning themselves for continued innovation in an increasingly data-driven diagnostic environment.
sauce:
Reference magazines:
Yao, K., Li, Z. (2026). A lifecycle framework for digital pathology adoption: Integrating infrastructure, workflow, regulatory compliance, and AI readiness for sustainable adoption. Journal of Clinical Pathology. DOI: 10.14218/jctp.2026.00004. https://www.xiahepublishing.com/2771-165X/JCTP-2026-00004
