Journal

A practical framework for the costs, funding models and continuity questions behind free medical AI, without assuming undisclosed commercial arrangements.

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.

Why evidence-supported medical AI answers can fail in practice, and a proposed response format that separates evidence, resources and unresolved decisions.

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.

Choose a medical learning subscription by the problem it solves, with a clear distinction between content access, specialist teaching, feedback, simulations and learning records.

Use a five-minute reading routine to identify a worthwhile paper and a focused clinical question, while keeping discovery, summaries and full appraisal distinct.