Journal

A proposed protocol for testing clinical AI consistency across repeated runs, paraphrases and meaningful changes without inventing test results.

How to determine whether clinical AI evidence still applies after changes to the model, sources, users, interface or authorised workflow.

What simulated patients can reveal about clinical AI, what they cannot establish, and how to interpret simulation research and educational practice.

Why medical examination scores do not validate autonomous care, and how to match clinical AI evidence to knowledge, workflow and patient outcomes.

What clinical AI should disclose when sources, models or action services fail, and how to distinguish a safe fallback from a silent downgrade.

How shared devices, persistent sessions and saved conversations can expose clinical information, and what a workable account handover requires.

How to review clinical AI permissions across records, drafts, messages and actions, with access limited to the task that was actually authorised.

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

How to assess whether clinical AI evidence applies to pregnancy, children and other underrepresented patients before relying on an overall score.

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

Clinical speech AI needs testing across accents, languages, interpreters and communication needs, with clinical meaning assessed separately from word accuracy.

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