Diagnose common failures: detecting image defects

Name failures specifically. Dr. Ada Okonkwo refuses the single bucket “the AI messed up” when discussing detecting image defects at Cedar Clinic. Separate data problems, task-framing problems, interface problems, and governance problems. Each needs a different repair, and only some involve retraining—keep the standing case (assign transcription to software but keep diagnosis with clinicians) in the room.

During a real interruption at Cedar Clinic, Dr. Ada Okonkwo stress-tests “Radiology-adjacent overclaim in a vendor pitch” on detecting image defects: one queued question, one hurried call, one hallway challenge. If the idea only works in a quiet workshop, it will not survive the standing case (assign transcription to software but keep diagnosis with clinicians).

Build a tiny failure gallery with detecting image defects and negotiating a family disagreement. For each, describe a plausible confident mistake, the first human who should notice at Cedar Clinic, and a fix that is not “ask it again.” Plausibility matters: cartoon failures do not train judgment for Dr. Ada Okonkwo.

Count something crude about detecting image defects—misses last week, minutes lost, or people affected—and write the number beside negotiating a family disagreement. Dr. Ada Okonkwo needs that comparison before anyone at Cedar Clinic declares victory on the standing case (assign transcription to software but keep diagnosis with clinicians).

List warning signs around detecting image defects that justify slowing public claims even if a pilot continues privately: missing owners, no logged overrides, identical outputs for dissimilar people, vendors who will not state training scope. When several signs coincide, freeze marketing language tied to the standing case (assign transcription to software but keep diagnosis with clinicians).

On “Rubber-stamp review after the fact”, Dr. Ada Okonkwo edits language about detecting image defects the way an editor would: strike “sentient,” “infallible,” and “just a tool” wherever they hide responsibility inside Cedar Clinic. negotiating a family disagreement stays nearby as a plain-language control.

Write one stop condition for detecting image defects with authority attached—a named role at Cedar Clinic who can pause use. Stop conditions without authority are theatre. Dr. Ada Okonkwo gets initials on the page before the next launch review, and uses negotiating a family disagreement to show what “pause” looks like in a simpler system.

For “Confusion between defect detection and diagnosis”, a second person at Cedar Clinic challenges Dr. Ada Okonkwo’s note on detecting image defects and asks whether negotiating a family disagreement already solves most of the need with less mystery. That challenge is part of finishing the standing case (assign transcription to software but keep diagnosis with clinicians), not a delay tactic.