AI is transforming medical imaging but it's also reshaping how diagnostic errors are judged in court. When an algorithm correctly identifies what a physician misses, the legal consequences go beyond the mistake itself. The workflow matters. The documentation matters. Even the sequence in which AI output is consulted matters.

This article explores what radiology already reveals about the "AI penalty," why digital pathology is next, and what physicians can do to make their use of AI legally defensible.

The success of companion diagnostics in oncology depends not only on the biomarker itself, but on how accurately it is interpreted. As IHC-based CDx testing becomes standard of care, the need for high-quality, accessible training resources for pathologists has never been greater. This article provides a curated overview of leading biomarker education platforms, with a focus on digital pathology and image-based learning.

Digital pathology is connected but not always cohesive.
Behind the rapid technological progress lies a structural interoperability gap that limits scalability, efficiency, and innovation. This article explores why technical connectivity is not enough, what true interoperability really means, and why it must be deliberately designed to unlock the full potential of digital pathology.