AI tools usually work by comparing your inputs—diagnosis, body part, time missed, and treatment history—to patterns. The problem is that two workers with the same diagnosis can have very different outcomes depending on:
- How quickly the injury was reported and whether the early record matches your later description.
- Whether your restrictions are documented consistently (and whether they come from treating providers, not just short notes).
- Whether the wage impact is supported by payroll and benefit records—not just your recollection.
- How the insurer frames “causation” (i.e., whether they believe the work event caused the condition).
In Inkster, where many people commute to nearby job centers and may work shifts that overlap with peak traffic, gaps in treatment or delayed paperwork are more common than people expect. Those gaps can affect how carriers evaluate credibility and permanence.


