About This Architecture

Digital pathology workflow for basal cell carcinoma (BCC) detection integrates whole slide imaging (WSI) scanners with AI-powered analysis on AWS infrastructure. The sequence spans three phases: slide acquisition from WSI Scanner to Digital Storage, AI Processing Phase with pre-processing and BCC Detection Model generating probability heatmaps across tissue tiles, and Human Review Phase where pathologists evaluate AI outputs including confidence scores and visualizations before finalizing the Diagnostic Report. This architecture demonstrates best practices for augmented intelligence in clinical pathology, maintaining human oversight while accelerating cancer diagnosis. Fork this diagram on Diagrams.so to customize the AI model stack, add DICOM integration, or map your own medical imaging pipeline with drag-and-drop AWS service icons.

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How do you architect an AI workflow for basal cell carcinoma detection in digital pathology on AWS?

A BCC detection workflow sequences WSI Scanner acquisition into Digital Storage, routes images through AI Pre-processing and a BCC Detection Model generating probability heatmaps, then delivers AI Outputs with confidence scores to Pathologist Review for final Diagnostic Report approval, ensuring human oversight in clinical decision-making.