In architecting safe-by-design systems, trustworthy AI must be addressed at every stage of the development lifecycle. One of the most persistent assumptions is the absolute certainty of medical annotation.
Metadata is often used as ground truth in medical imaging AI, but its availability does not guarantee its reliability. This article explores how a metadata traceability assessment can reveal inconsistencies, missing information and manufacturer-dependent limitations before model development begins.
Two teams can work on the same medical imaging task and end up building very different systems. The difference often starts before any model is trained, at the moment the problem is defined. This article explores what problem framing actually means, where ambiguity comes from, and what a well-framed problem changes in practice.
The Data Bottleneck in Medical Imaging Medical imaging AI has achieved remarkable progress, but most breakthroughs still rely on massive, labeled datasets: a luxury often out of reach in healthcare….