Journal

Clinicians will need deep medical knowledge, evidence appraisal, patient communication and practical skills for supervising AI-assisted work.

Clinical software may benefit from specialised models and ordinary rules, but model routing and handovers need their own evaluation.

A clinical AI orchestration layer could coordinate models and tasks, but authority, task state and recovery matter more than owning every component.

A practical framework for testing healthcare AI defensibility across evidence, distribution, workflow, trust and professional learning.

The clinician's AI desktop may be shaped by record access, permissions and distribution as much as by model quality, with several plausible market outcomes.

The OpenEvidence and Johns Hopkins collaboration raises practical questions about reasoning, assessment and learning when medical evidence is easier to retrieve.

Healthcare AI investment targets different layers of care, but funding, valuation and planned spending must not be confused with realised clinical or financial returns.

OpenEvidence's $15 billion valuation makes precise usage definitions commercially consequential. The question is how physician activity becomes durable revenue and defensible clini...

Compare Doctolib and OpenEvidence through workflow access, clinical intelligence, business models and the evidence needed before their strategies converge.

Doctolib and OpenEvidence illustrate how clinical AI is converging with records, workflows and education, without making integration a substitute for evidence.

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

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