AI doesn’t “make the diagnosis” the way people sometimes imagine. More often, it affects the workflow around diagnosis—what gets noticed, what gets prioritized, and what gets recorded.
In Albuquerque, diagnostic error patterns we see frequently include:
- Imaging and report turnaround issues (where a result may be delayed, overlooked, or communicated too late for meaningful intervention)
- Triage and risk-sorting problems in high-volume settings, where symptoms that don’t fit a typical pattern can be underweighted
- Lab result handling and follow-up breakdowns, including abnormal findings that aren’t escalated or clearly communicated
- Multi-provider fragmentation, when one clinic has part of the story and another provider makes decisions without the complete context
- Automated documentation assistance that creates an incomplete narrative—sometimes making it harder to prove what was actually seen, ordered, and communicated at the time
If your care involved decision-support software, predictive analytics, or any automated recommendation used during triage, imaging review, or documentation, it may be relevant to your claim—but the legal focus is still on what reasonable clinicians and facilities would have done with the information available.


