Journal

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.

An accurate AI referral letter is only one step: assess destination, acceptance, changing urgency and follow-up before calling the pathway complete.

AI result management needs explicit clinical ownership, visible unresolved work and confirmation that follow-up happened, not merely that a message was sent.

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

How to investigate a disputed AI clinical note when audio is unavailable, without dismissing the patient or rewriting the record's history.

How AI summaries can change the certainty of a diagnosis or plan, and how clinicians and developers can preserve its original meaning.

Why AI clinical notes must preserve who spoke, who they meant and what they knew, with practical tests for multi-speaker consultations.

An AI scribe must preserve clinical meaning, uncertainty and attribution, because faithfully captured words can still become a misleading summary.

Medicines questions require verified patient information, product-specific sources and checked calculations, not just a persuasive AI explanation.

Why AI use should be understood in the context of access barriers, with practical safeguards for escalation, continuity and patients who still need care.