The increase in cancer incidence, combined with rising testing complexity and staffing shortages, has placed significant strain on pathology laboratories. Many see digital technologies, such as AI-powered digital pathology, as a path forward, helping to alleviate resource constraints while improving efficiency and accuracy. However, as interest grows, questions remain around the reproducibility, scalability, and reliability of end-to-end digital pathology workflows, particularly in high-volume settings.
This application note presents findings from a proof-of-concept study that tested an open, multivendor digital pathology workflow under high-throughput conditions. It provides key performance metrics, including success rate, scanning and processing times, throughput, and repeatability, to help pathology labs better understand how these systems perform in real-world clinical settings.
Download this application note to explore:
- How digital pathology workflows perform in high-throughput laboratories
- Insights into the technical and significant repeatability of AI image analysis
- Performance data related to the consistency and robustness of digital pathology
- Key considerations when evaluating end-to-end digital pathology solutions
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