By Lisa-Jean Clifford
Many laboratories evaluating AI in digital pathology ask the wrong first question: Does the model work? Accuracy matters — but most AI initiatives don’t stall because of performance metrics. They stall because the technology disrupts the diagnostic workflow pathologists rely on every day.
If AI requires switching platforms, adding manual steps, or reworking case workflows, it becomes another layer of friction. Pathologists don’t reject AI because they oppose innovation — they reject tools that slow them down or introduce uncertainty. The laboratories seeing measurable gains are embedding AI where it strengthens decision-making: ROI identification, quantification and grading during interpretation.
AI is not a plug-and-play add-on. It’s an operational decision. Define the workflow objective first — quality control, identification, calculations, standardization — and integration becomes strategic rather than experimental.
For a detailed breakdown of what responsible, scalable integration actually requires, read the full guide: AI in Digital Pathology: A Practical Guide to Workflow Integration