Journal

CME and CPD are moving inside the tools clinicians already use, with credits earned from real clinical questions; the shift makes lifelong learning continuous, and it makes the qua...

OpenEvidence is a remarkable clinical answer engine, but medical students need retrieval practice, calibrated feedback and exam-shaped coverage, which an answer engine alone was ne...

OpenEvidence, AMBOSS, GPnotebook, UpToDate and Heidi are all converging on the same insight: answering clinical questions is table stakes, and the durable value lies in helping cli...

AI search makes finding answers nearly effortless, but retention is built by effortful retrieval, which is exactly what searching removes; understanding that difference is the key ...

AI can explain, quiz and tutor, but the evidence on retrieval practice, spacing and calibrated difficulty says structured question banks still do a job conversational AI alone does...

OpenEvidence is layering CME, society-produced educational content and evidence grading onto its answer engine; here is what the education turn by the world's most used medical AI ...

A decision tree for choosing DGM revision resources by time, budget and learner profile, anchored to the RCP blueprint and honest about which jobs a question bank cannot do.

A decision tree for choosing DOccMed resources by learner profile, time and budget — with an honest read of the thin market, a minimum stack, and three worked candidate examples.

Frailty assessment, mental capacity, medicines optimisation and multidisciplinary coordination are DGM skills a best-of-five bank cannot score — here is how to train and test them ...

A decision tree that picks SCE Palliative Medicine resources by your time, budget and learner profile — using an honest measurement input rather than declaring one universal best b...

There is no single best DipIMC resource — there is the right stack for your time, budget and learner profile, and the biggest error is buying more Part A questions when the OSPE is...

A decision tree for choosing DTM&H resources by learner profile, time and budget — with a minimum stack, budget and time bands, and three worked candidate examples.