A newly published manifesto in Diagnostic Pathology outlines a practical framework for integrating digital twin (DT) technology into clinical pathology laboratories. Authored by an international group of pathologists and engineers, the paper provides a comparative analysis of traditional and DT-enabled laboratory workflows, with emphasis on diagnostic efficiency, operational performance, and implementation feasibility.
Digital twins are real-time virtual models of physical systems, widely used in industries such as manufacturing and logistics. When applied to pathology, these models can simulate, monitor, and optimize lab operations – from specimen accessioning to diagnosis and archiving. The authors describe how DTs may help address persistent challenges in pathology labs, including labeling errors, process inefficiencies, and diagnostic delays.
The proposed DT framework divides pathology workflow into key stages: accessioning, grossing, processing, embedding, cutting, staining, scanning, diagnosis, and archiving. For each phase, the authors identify potential DT benefits based on adaptations of proven industrial use cases. For example, in accessioning, DTs could simulate and track specimen routing in real time, reducing labeling errors by up to 90 percent and increasing throughput by 15-20 percent. At the staining stage, predictive modeling of reagent behavior may cut staining inconsistencies by 40 percent.
In the diagnostic phase, the use of digital patient and tissue “twins” – AI-assisted virtual representations of samples – could improve diagnostic accuracy and shorten turnaround times by up to 50 percent. This approach may also support lesion prediction and pre-screening, potentially streamlining the diagnostic process for pathologists.
Beyond efficiency, DTs could aid in inventory management, predictive maintenance of lab equipment, and compliance tracking. A diagram on page 5 of the article illustrates how DTs could influence each workflow component – from automating data entry to digitizing slide analysis and optimizing archiving logistics.
The authors acknowledge barriers to widespread adoption, including high initial costs (estimated at USD 100,000–200,000 for medium-sized labs), staff resistance to digital workflows, and concerns around data privacy and security. To address these issues, the paper suggests phased implementation beginning with high-impact areas, integration with existing laboratory information systems (LIS), and targeted training for staff. A 12–24 month rollout is recommended.
The paper calls for additional studies to evaluate long-term clinical and economic impacts of DTs in pathology, particularly regarding AI use in diagnostic decision-making and patient data governance.
While the authors emphasize that DT integration is still in early stages, they present this manifesto as a foundational guide for laboratories seeking to adopt predictive, simulation-based models for pathology operations.
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