Journal

How incremental feature changes can outgrow an AI scribe's original approval, and what NHS teams should reassess before drafting becomes action.

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

Why model benchmark scores do not automatically translate into better clinician performance, and how to evaluate the actual human-AI workflow.

How an inaccurate clinical statement can become established history through repeated summaries, and how to trace and correct the downstream record.

Why agreement between medical AI systems is not independent verification, and how source checks, deterministic tools and clinical judgement can work together.

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

A whole-workflow cost model for clinical AI, separating subscription fees, verification effort, released capacity and genuine cash savings.

Why an AI error percentage needs a unit, severity definition and exposure context before clinicians or NHS buyers can interpret it.

Healthcare agents turn information into operational consequences, requiring explicit permissions, completion evidence and shared clinical and organisational accountability.

Clinical AI may improve immediate performance while changing learning opportunities; examine the evidence for deskilling and the cognitive demands of integrated workflows.

A four-stage method for checking whether clinical AI citations exist, support the claim, remain current and apply to the patient and setting.

Clinical AI can omit an exception, uncertainty or follow-up step while every sentence it includes sounds correct: here is how to check completeness.