Evidence before procurement
Use the following questions to structure a review of a laboratory workflow, whether it serves a commercial testing network, university core or captive in-house laboratory. They are due-diligence prompts, not findings about a particular product.
1. Which scanner, image format and intended use are covered by the evidence?
Request version-specific documentation, test conditions, responsible reviewers and the limits of each claimed result. Keep announced plans separate from observed performance.
2. Does the regulatory record describe the imaging system, the algorithm or both?
Request version-specific documentation, test conditions, responsible reviewers and the limits of each claimed result. Keep announced plans separate from observed performance.
What the primary record supports
The FDA notice distinguishes technical imaging assessment from clinical submission data. This historical notice is not an AI-specific clearance.[1]
- [1] Technical Performance Assessment of Digital Pathology Whole Slide Imaging Devices — availability notice · FDA / Federal Register · Original publication: 2016-04-20 · Accessed November 7, 2026.
Source reference reviewed by AI; no human author or reviewer is claimed. This evergreen research lens is not a news report or validation certificate.
Commercial and operating implications
An independent laboratory should specify the decision it expects a system to improve before comparing proposals. Define the workflow boundary, data ownership, audit trail, staff responsibility and exit path. Request a documented pilot and reconcile claimed savings with actual implementation, training and maintenance costs. These are analytical evaluation questions; no benchmark savings or vendor rankings are asserted.
For investors, a partnership announcement and recurring contracted revenue are different evidence categories. Seek disclosed deployment, customer or financial data before attributing revenue to AI. For public laboratories, distinguish funding authorization, procurement and operational commissioning.
Continue the research
- Laboratory AI validation →
- LIMS integrations & robotic sample preparation →
- Autonomous labs & instrument orchestration →