AI tools typically ask for details like injury severity, treatment duration, and categories such as medical expenses and lost income. They may generate a range or a rough framework.
That can be helpful when:
- you want to understand what evidence usually supports damage categories,
- you’re organizing documents and want a checklist-like structure,
- you’re trying to gauge whether your claim is likely to involve more than “early” medical visits.
But in Airmont-area truck cases, an AI estimate often misleads when it can’t account for the kinds of proof that matter most locally, such as:
- whether your treatment timelines match the mechanism of injury,
- disputes over whether symptoms worsened due to the crash (or were pre-existing),
- evidentiary gaps—like missing records from the trucking company or incomplete documentation after the collision.
A number generated by software can’t read your imaging reports, review the crash report narrative, or evaluate what an insurer is likely to challenge.


