Implementation Challenges in AI for Pathology

In order to achieve ubiquitous success in AI for Pathology around the world (in particular in histopathology), a number of key technical issues and challenges need to be addressed. These - and potential remedial approaches - will be explored in this research program and they include (as examples): 

• variability in staining outcomes across different stains and different sites (even within a given Pathology company or service)
• prevention and management of artefacts in digital pathology slides (eg - poor focus, external artefacts, air bubbles) 
• management of changes in AI model types and performance over time 
• challenges in managing data volumes (especially given the 3D format), and impacts related to digital slide and storage formats
• the relationship of these issues to Quality Assurance programs in laboratory medicine