AI, on one page.

What it is, where it bites, what it costs, who signs, and where it sits in healthcare.

1
Ask what goes in and what comes out. If nobody can say, there is no project yet.
2
Most of the value is in simple models you can explain in a sentence.
3
The only score that counts is on data the model never saw. Ask for it.
4
Fluent is not the same as true. Keep a person wherever the answer must be exact.
5
Know what a false alarm costs and what a miss costs. The threshold is your decision, not the vendor’s.
6
Autonomy is earned from the logs, one notch at a time. Start with a person on nearly everything.

Where it sits in healthcare

Step	What the AI does	Lands in	Ask
Schedule A simple model	Predicts which appointments are likely to be missed, so slots can be backfilled.	The schedule and the waitlist	What goes in, and how wrong was it last month?
Intake Where it bites	Flags patients who may need an earlier visit.	Intake priority flags	Was it tested on patients from a site it never saw?
Visit The assistant	Drafts the visit note from the conversation for the clinician to edit.	A draft note in the health record	Does a clinician sign every note, and is the draft marked as a draft?
Code Who signs	Suggests billing codes; confident ones go for one-click confirmation, the rest to a coder.	The billing queue	Who confirms each code, and what share do they actually check?
Claim The price of wrong	Flags claims likely to be denied before they are sent.	Claim review before submission	What does an extra review cost against a denied claim?

The three questions that settle any AI pitch

1
What goes in, and what comes out?
No clear answer, no project. Yet.
2
How did it score on data it had never seen, and on how many rows?
The demo score is the student marking their own exam.
3
What does a wrong answer cost, and who signs the ones it gets to make alone?
The threshold and the autonomy are your decisions, not the vendor’s.

Illustrative examples, not client results. Nydros · nydros.com/learn/brief?industry=healthcare