Journal

A practical consultation approach to AI-curated medical papers: assess applicability, uncertainty and patient priorities without dismissing the research effort.

What Advoca states about optional research, service analytics and rewards, and the questions clinicians should ask before signposting a patient-owned health app.

Abridge Assistant offers CME for eligible questions, but credit, useful answers and retained knowledge are different outcomes. Explore a stronger point-of-care learning loop and wh...

Healthcare AI consolidation may turn on complementary assets, integration rights and clinical capability rather than model performance alone.

Free clinical AI still has operating costs: compare advertising, enterprise contracts and paid education without confusing price with independence.

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

A fictional 2030 GP patient journey separates existing AI functions from developing capabilities and the governance needed before greater automation.

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

Clinical decision support can be strategically valuable, but its economics depend on the task, evidence, workflow and who captures the benefit.

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.

How to plan an exit from a healthcare AI supplier while preserving usable records, unresolved work, staff capability and continuity of care.