Many people assume AI is either “the cause” or “not relevant.” In real cases across Palos Heights and nearby Chicago-area communities, the more common story is that automated systems become part of the clinical workflow—and then the human oversight breaks down.
Examples we see in practice include:
- Imaging and report workflows: AI-assisted prioritization or highlighting that leads a clinician to treat a result as more certain than it is.
- Risk scoring and triage decisions: automated flags that route a patient one way—while other red flags get overlooked.
- Lab and follow-up gaps: abnormal results that should trigger rapid follow-up but get delayed by workflow handoffs.
- Charting and documentation assistance: incorrect or incomplete entries that affect later decision-making.
The key point for a claim is not whether a tool exists—it’s whether the care team used it appropriately, verified it against objective findings, and acted on concerning information in time.


