·overview
Why clinical AI works in the lab but stumbles at the bedside
A model can score brilliantly in a paper and still fail on your ward. The reasons are predictable, and knowing them is the first step to using AI well.
Series
AI in Healthcare
How software and AI are changing clinical work, and how to judge what actually helps. Evidence-led guides for clinicians on evaluating, deploying, and governing clinical AI, from diagnostic models to documentation and coding tools. Read in order, or jump to the topic you need.
6 posts · read in order for the best benefit
How to evaluate a clinical AI tool before you trust it
AUC is not enough. Here are the questions, metrics, and evidence that actually tell you whether a clinical AI tool is safe to trial.
Deploying clinical AI safely: integration, monitoring, and governance
Choosing a good model is the easy part. Deploying it safely is a systems and governance job, and it is where most clinical AI projects quietly fail.
Ambient AI scribes: the clearest near-term win, and its limits
Of all the ways AI is entering clinics, ambient documentation is the one clinicians actually feel. Here is what it does well, and where you still have to be careful.
AI for coding, billing, and prior authorization
The paperwork clinicians resent most is exactly where AI is advancing fastest. A look at coding, claims, and prior authorization, including the India picture.
A clinician's field guide to adopting AI
The whole series in one practical guide: where to start with clinical AI, what to demand, who stays accountable, and how to avoid the predictable traps.