Digital pathology (DP) is transitioning from an adjunct technology to an enterprise diagnostic platform in the United States. Despite accelerating clinical adoption, many laboratories face persistent barriers, including high capital and operating costs, workflow disruption, interoperability challenges, and a complex regulatory and reimbursement environment. This narrative review proposes a practical lifecycle framework for implementing and sustaining DP programs, with an emphasis on defining and operationalizing institutional artificial intelligence (AI) readiness for safe and sustainable adoption.
Methods
We performed a targeted narrative review informed by searches of PubMed/MEDLINE and Google Scholar for English-language publications from January 1, 2014 through December 31, 2025. Core search concepts included DP, whole slide imaging, image management/viewing systems, laboratory information system integration, validation, reimbursement, U.S. Food and Drug Administration clearance, Clinical Laboratory Improvement Amendments oversight, College of American Pathologists accreditation, interoperability standards, cybersecurity, and AI. We supplemented database searches with reference screening and review of primary guidance and public databases from regulatory and professional organizations in the United States. We prioritized peer-reviewed literature and used web-based regulatory sources when they represented the authoritative primary reference. We also incorporated our professional experience and knowledge in DP and AI.
Results
Key implementation domains span foundational infrastructure (scanners, storage/networking, and integrated image management platforms), workflow redesign across pre-analytic, analytic, and post-analytic phases, validation and quality management, regulatory compliance and accreditation, cost capture, 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 deployment.
Conclusions
Digital pathology implementation extends beyond scanners and data storage. Durable success requires a comprehensive, lifecycle-oriented framework integrating infrastructure, workflow redesign, validation, interoperability, AI readiness, security, education, performance monitoring, and institutional governance. By addressing these domains proactively—and by explicitly distinguishing device authorization from laboratory validation and operational quality management—pathology departments can realize the clinical and operational benefits of digital pathology while positioning themselves for continued innovation in an increasingly data-driven diagnostic landscape.
Full text
https://www.xiahepublishing.com/2771-165X/JCTP-2026-00004
The study was recently published in the Journal of Clinical and Translational Pathology .
Journal of Clinical and Translational Pathology (JCTP) is the official scientific journal of the Chinese American Pathologists Association (CAPA). It publishes high quality peer-reviewed original research, reviews, perspectives, commentaries, and letters that are pertinent to clinical and translational pathology, including but not limited to anatomic pathology and clinical pathology. Basic scientific research on pathogenesis of diseases as well as application of pathology-related diagnostic techniques or methodologies also fit the scope of the JCTP.
