In real cases, the issue is usually how information flowed through the system—who reviewed what, when, and what happened after the tool flagged a risk.
AI-related diagnostic problems may involve:
- Imaging and radiology support used to prioritize or interpret findings
- Risk scoring or triage tools that influenced urgency and routing
- Lab workflow software that affected how abnormal results were flagged or communicated
- Charting/documentation assistance that influenced what clinicians believed was happening
Even when a tool produces a “likely” outcome, clinicians still have a duty to verify and apply clinical judgment. If the team treated an automated output as definitive—without resolving conflicts with objective findings—the gap can become legally significant.


