Journal

Accessible healthcare AI must support understanding, input, correction and human help, not simply display readable text on a screen.

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

Assess AI prescribing across identity, information, authorisation, dispensing and follow-up, rather than treating a suitable medicine as the whole decision.

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

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

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

How accurate clinical facts become misleading when patient identity, chronology, record status or units are lost during AI-assisted work.

Why patient access to AI-generated notes should complement professional checking, with a practical route for reporting and correcting errors.

Practical scripts for explaining AI scribes, clinical reference searches and digital triage without claiming infallibility or complete security.

A practical test of whether refusing an AI tool leaves patients with a credible alternative, rather than a slower or less accessible route to care.

What patient research shows about AI recording, sensitive disclosures and the practical safeguards that could protect honest consultations.

Healthcare AI retention should distinguish raw audio, transcripts, approved records and safety evidence, with justified purposes rather than one universal deletion period.