Diagnostic errors don’t always look like a clear-cut “mistake.” More often, they show up as a chain of decisions that—together—led to the wrong outcome.
In our experience with cases across the Atlanta metro, problems frequently involve:
- Delayed escalation after abnormal results (for example, concerning lab values or imaging findings not acted on quickly enough)
- Automation-driven triage that routes a patient to the wrong level of urgency
- Imaging or report interpretation workflows where an automated flag influences what a reviewer focuses on (or fails to notice)
- Incomplete documentation of symptoms, risk factors, or patient history—especially when intake is shortened to fit busy clinic throughput
- Follow-up failures after the first visit, when the clinical plan depends on results that weren’t properly tracked
AI tools can be used responsibly, but when they’re relied on too heavily, applied outside their limits, or not verified against objective findings, the issue can become legally relevant.


