AI estimators typically produce a range based on generalized patterns. That’s helpful, but it can break down quickly in catastrophic injury claims, where small differences in proof can change the outcome.
In Mount Vernon, two factors commonly affect how value is argued:
- Traffic and impact scenarios: Rear-end and intersection collisions, common during commute hours, can involve disputed timing, braking, and witness perception—issues that later affect how insurers evaluate fault and causation.
- Pedestrian and crosswalk risk: When a spinal injury follows a pedestrian incident, documentation about crosswalk signals, visibility, and where the impact occurred becomes critical. An AI tool can’t interpret surveillance footage or police reports.
New York settlement discussions also reflect liability risk and evidentiary readiness. If the medical record and accident proof don’t line up cleanly, insurers often resist higher numbers—even when the diagnosis sounds similar on paper.


