AI-Adjusted Standard of Care (AASC) — Part 2
After the last post, a few people asked a very reasonable question:
“What does that actually mean?”
Traditionally, standard of care is defined by:
- training
- experience
- peer behavior
- expert testimony
It evolves slowly.
But AI introduces something new:
→ real-time pattern recognition
→ population-level data
→ continuous feedback
Now imagine this:
An AI system can show that:
- 78% of comparable cases are treated one way
- Complication rates drop when protocol X is followed
- Certain deviations correlate with higher risk
At that point, something subtle happens.
The “standard” is no longer just:
→ what clinicians say
It becomes:
→ what the data shows
This doesn’t eliminate clinical judgment.
But it does change the environment around it.
- Deviation becomes visible
- Variation becomes measurable
- Decisions become comparable
And that raises a harder question:
If AI can define what is “typical”…
does it begin to define what is “acceptable”?
We’re not there yet.
But we’re closer than most people think.
And the systems we build now will determine:
→ whether AI supports clinical judgment
or
→ quietly replaces it
More on that next.