31 models.
100 real consults.
ARISE Network. 2026.
Severe harm in 22% of cases.
77% of those harms were not wrong answers.
They were things the model
never said at all.
Clinical AI is not failing loudly.
It is failing silently.
Every week the same room.
Deployment approved.
Vendor gone.
Staff using the tool.
Board has seen the benchmarks.
Nobody has opened the data layer.
When they finally do.
Training sets that do not match
the patients being seen today.
No named owner
for what the model was taught.
No audit trail
for what it was never shown.
The model does not know
what it missed.
It was never trained to know.
Most institutions move toward deployment
hoping the AI closes the gaps
underneath it.
It does not close them.
It operationalizes them.
The Governance Owner
names what the model is authorized
to learn from.
↳ Charters the data boundary.
↳ Commissions the training standard.
↳ Covers the institution when challenged.
The Decision Owner
holds what reaches the bedside.
↳ Decides which output the clinician acts on.
↳ Documents what the chart will carry.
↳ Defends the call when the audit arrives.
The Handoff between them
is where the data layer
gets governed or gets skipped.
The Accountability Gap™ (TAG™)
does not open at the point of care.
It opens in the room
where nobody is standing.
You already know which deployment
has no named owner
for what the model was taught.
That is not a data problem.
It is an accountability problem
wearing a data problem's clothes.
What is the clinical data truth
your institution has been quietly avoiding
before the next deployment goes live?
Mo Johnson, MD MBA is a cardiothoracic surgeon and the founder of GPe Research. Field Notes are short dispatches from the clinical AI accountability frontier, published alongside the MedicoVigilance™ newsletter at medicovigilance.org.
Follow the work on LinkedIn: linkedin.com/in/mo-johnson