Journal

Compare Nolla Health, Heidi, OpenEvidence and iatroX by who uses the AI, what it produces, and who is authorised to act on the output.

Jev's low advertised input price could make more clinical AI checks feasible, but total value depends on evidence, review and completed workflows.

Jev and OpenEvidence address different layers of clinical AI: bounded software decisions versus clinician-facing evidence search and synthesis.

Jev returns bounded decisions rather than medical answers, creating possible roles in clinical routing, evidence selection and source checking.

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

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.