AI tools typically work like this: you enter basic details (injury type, date, treatment, missed work), and the tool returns a rough settlement range based on generalized patterns.
In College Park, that kind of estimate can go off-track for reasons that aren’t “math errors” so much as case-development issues. For example:
- Injury reporting and witness details: In busier corridors and mixed-use areas, the incident narrative can become the battleground. If the adjuster believes the timeline is inconsistent, they may discount the claim.
- Medical records that don’t line up with restrictions: A treatment plan might exist, but if work limitations aren’t documented clearly (or repeatedly) the insurer may argue the impact was temporary.
- Return-to-work pressure: Employers and insurers sometimes encourage early return, especially when jobs require mobility. If you returned before your restrictions were stabilized, later gaps in documentation can create leverage for the insurer.
AI can’t reliably judge those dynamics. It doesn’t review the evidence the way Maryland claims are actually evaluated.


