Journal

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

Scarlet's funding and the September 2026 AI regulation recommendations raise practical questions about evidence, monitoring and responsibility after deployment.

A proposed protocol for testing clinical AI consistency across repeated runs, paraphrases and meaningful changes without inventing test results.

Why medical examination scores do not validate autonomous care, and how to match clinical AI evidence to knowledge, workflow and patient outcomes.

How misleading or manipulated medical content could enter AI answers, and why source origin, support and applicability need separate checks.

Healthcare AI retention should distinguish raw audio, transcripts, approved records and safety evidence, with justified purposes rather than one universal deletion period.

Clinical queries, account details and support logs can reveal sensitive information even outside an EHR, so reference tools need proportionate privacy scrutiny too.

Clinical AI data collected for individual care cannot be assumed available for every later purpose: assess quality improvement, research, training and product development separatel...

UK hosting is one part of a healthcare AI assessment, not proof of safety: map processing, remote access, subprocessors, logs and the applicable transfer arrangements.

A patient-centred response to healthcare AI harm needs clinical remediation, clear communication, coordinated investigation and distinct routes for complaints and redress.

Document the material clinical reasoning, verification and final plan when AI contributes to care, without replacing the record with chatbot transcripts.

Clinical AI disclaimers matter, but their effect depends on the claim, law, product design and actual use; they are not an automatic shield against liability.