Journal

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.

Separate literature retrieval, an evidence overview and active clinical learning when choosing between PubMed, OpenEvidence and iatroX.

Compare OpenEvidence and iatroX by access, licensed sources, jurisdiction, follow-up learning and the exact clinical task.

An active clinician is a defined person doing a defined task within a defined period. Anything less leaves the most important part of adoption unstated.

Clinical AI expansion can involve internal development, partnerships or acquisitions, but the useful test is whether the combined experience completes the customer's work.

Clinical AI evidence tools compete on both answer quality and where questions begin, but integration, patient context and source support need separate evaluation.

Compare dated funding milestones for Tandem, Heidi, OpenEvidence and Abridge, and the different clinical problems their investment plans address.

A three-country GPT-4o trial improved doctors' vignette scores, but its restricted control group limits what the findings say about routine care.

OpenEvidence's oncology work predates Osler, Sackett and Snow: separate specialist sources, model design and research depth when assessing the September launch.

Understand OpenEvidence's named models, what the Anthropic partnership establishes, and which technical questions still require explicit documentation.