Today’s healthcare systems may use software to assist with triage, risk scoring, imaging workflows, or documentation. Those tools can be helpful—until they’re treated as a shortcut for clinical judgment.
In a Paris-area setting, a common pattern we see is this: a patient presents symptoms, the care team documents findings, automated systems flag a likelihood of a condition (or fail to flag urgency), and the next step proceeds—sometimes without the level of verification that the situation required. If the diagnosis later proves incorrect or late, families are left wondering whether the system’s output influenced decisions.
A lawyer’s job isn’t to “blame AI.” It’s to examine how the care team responded to the information they had at the time:
- Were abnormal results escalated appropriately?
- Were symptoms re-evaluated when the patient returned?
- Did documentation match the clinical reality?
- Were automated recommendations treated as advisory—or effectively treated as definitive?


