What each AI use case actually changes
Most published healthcare AI use cases are a paragraph of benefits and a vendor logo. Every entry here has to fill seven fields before it is allowed to exist: who owns it, what you measure before you start, an effect size with a citation you can open, what changes in the workflow, three ways it fails, its governance risk tier, and the conditions under which the honest answer is don't.
An entry that cannot fill all seven from real evidence is not written. That rule is enforced at build time, not by intention.
AI-Assisted Mammography Screening
How AI-supported mammography reading increases cancer detection rates, reduces interval cancers, and cuts radiologist workload.
AI-Assisted Medical Coding for Revenue Cycle Management
How NLP-driven AI coding tools improve ICD-10 coding accuracy, reduce claim denials, and accelerate the revenue cycle.
AI-Driven Patient Scheduling and No-Show Prediction
How machine learning predicts appointment no-shows and enables targeted interventions to reduce missed visits and idle clinic time.
Ambient AI Scribes for Clinical Documentation
How ambient AI scribes reduce documentation time, after-hours charting, and clinician burnout in ambulatory care settings.
How to read one
The two fields most people skip are the ones worth reading first. Three failure modes is where the monitoring plan comes from — a failure you have named before deployment is one you can watch for. When not to do this is the field a vendor will never write for you, and it is usually the reason a pilot that worked somewhere else does not work here.
Every effect size links to its source. Where a number could not be sourced it is not printed, and the mechanism is argued without it.