Journal

Local medical AI needs more than national sources: it needs visible authority, meaningful clinical participation and a transparent way to handle disagreement.

Compare donated subscriptions, institutional access and conversational medical AI by the evidence and learning tasks they help clinicians complete.

A proposed paired-scenario benchmark for testing whether medical AI adapts appropriately to changes in resources, referral access and guideline context.

An original proposal for showing the jurisdiction, sources, supplied facts, assumptions and unresolved context behind a medical AI answer.

WHO SMART Guidelines explain why translating medical AI is different from adapting evidence, data requirements and care pathways for local use.

What the Penn Medicine and Botswana-UPenn partnership announcement shows about the difference between international access and locally developed clinical AI.

A practical learning pathway for wider medical AI access, with measures of retention, transfer and judgement rather than query counts alone.

Two competing explanations for the future of medical AI, and the evidence specialist platforms need to show value beyond access to a powerful model.

What the Anthropic and OpenEvidence partnership establishes, what remains unverified, and how clinicians can assess locally adapted medical AI.

Use US-oriented medical games and study resources thoughtfully by separating transferable concepts from UK terminology, pathways, prescribing context and examination requirements.

Build a focused emergency medicine learning toolkit around ECGs, prioritisation, imaging, diagnostic reasoning and communication, without confusing apps with supervision.

Choose clinical image practice by modality, task and feedback, with a selection matrix covering ECGs, CT cases, radiographs and ultrasound clips.