“AI misdiagnosis” isn’t usually one dramatic failure. More often, it’s a chain of decisions—where software inputs, documentation assistance, triage routing, or imaging/lab interpretation support played a role.
In real Burlington-area cases, the fact pattern often looks like one of these:
- Urgent care or same-day clinic workflow: triage notes summarize symptoms quickly, and abnormal findings aren’t escalated when they should be.
- Imaging or lab turnaround pressure: results come back after hours, then the next-step plan is unclear or not acted on promptly.
- Electronic documentation gaps: automated templates omit key history, or important symptom details aren’t carried forward between visits.
- Care transitions: a referral is placed, but follow-up depends on the patient catching it—something that can be difficult when you’re working, caregiving, or dealing with transportation barriers.
A lawyer’s job is not to argue that “AI is bad.” It’s to evaluate whether the medical team and facility met the standard of care—including whether automated outputs were verified and whether risks were communicated and acted on in time.


