Digital pathology (DP) is reshaping diagnostic workflows, offering enhanced accuracy, efficiency, and collaboration through high-resolution slide scanning, artificial intelligence (AI), and cloud-based infrastructure. In Tuscany, the adoption of DP is framed within a regionally integrated healthcare system composed of three local health authorities and four university hospitals, coordinated under a hub-and-spoke model. This structure supports the potential for widespread DP implementation, leveraging centralized expertise and shared digital infrastructure. A region-wide survey involving all public pathology centers in Tuscany confirmed that all institutions are already equipped with at least one whole slide scanner. Building on this foundation, the region has initiated a strategic transformation stepwise process to implement and progressively expand DP workflows. Key actions included the adoption of a common regional laboratory information system (LIS), the development of a centralized cloud repository ensuring secure data access, and the design of telepathology modules to enable remote consultations and second opinions. Dedicated training programs for technical staff and the progressive introduction of AI-assisted tools are also part of this roadmap, ensuring readiness for routine clinical integration. The benefits of DP in Tuscany are manifold: faster diagnostic turnaround, improved inter-institutional collaboration, standardized reporting, and opportunities for research and education. Integration of AI-assisted tools is expected to support routine diagnostics, especially in high-volume and complex cases. The regional network also creates a foundation for multi-omics integration and computational pathology research. To ensure successful implementation, the region adopted a phased, scalable approach backed by regulatory alignment and continuous professional development. A unified DP network with shared protocols and centralized resources will be crucial. Tuscany's experience may serve as a blueprint for other regions aiming to transition toward a digital, AI-powered pathology ecosystem aligned with the broader goals of precision medicine.
Holmes et al. (Wed,) studied this question.