Journal

How clinical AI activity data could become workplace monitoring, why usage does not prove competence, and what UK organisations should assess.

A practical approach to healthcare AI updates, covering change ownership, regression testing, security patches, rollback and unfinished actions.

A practical framework for testing clinical AI across access, decisions and outcomes without allowing averages to conceal health inequalities.

Clinical AI can mistake an incomplete record for a complete history: assess missing information, source coverage and safe routes to reconciliation.

A healthcare algorithm can predict its chosen target well while allocating care unfairly: examine the difference between cost, use and clinical need.

How to distinguish evidence-based agreement from agreeable AI behaviour, with neutral questions and a practical protocol for testing clinical reasoning.

Fast AI generation can create slow human verification: assess review effort, correction work and workflow design before claiming time savings.

A freshly generated AI answer may rely on outdated guidance, so assess recommendation versions, source updates and retrieval freshness separately.

Retrieval can improve clinical AI grounding, but failures in source selection, passage handling and synthesis still need separate testing.

An AI answer can be medically plausible yet locally unsuitable when it imports another country's guidance, licensing assumptions or care pathways.

How to distinguish confident wording, model probabilities and validated calibration before treating a clinical AI confidence display as a safety signal.

A transparent comparison protocol for OpenEvidence, AMBOSS and iatroX, focusing on source support, jurisdiction, uncertainty and the limits of product claims.