Injury claims aren’t evaluated off a diagnosis label alone. They’re evaluated based on what can be shown—timelines, documentation, and functional impact. That’s where AI outputs can go wrong.
For example, an AI estimate may assume:
- symptoms followed a typical course
- treatment was consistent
- cognitive issues were documented in a way an adjuster can rely on
- the accident mechanics support the type of head trauma alleged
But Cranston cases often come with details that don’t fit a generic model—like:
- symptoms that worsen during the return to commuting and work schedules
- delayed imaging or follow-up because initial symptoms looked “minor”
- gaps caused by difficulty tracking appointments during recovery
- disagreements about whether the head movement in a rear-end crash could plausibly cause the reported neuro symptoms
AI may organize variables, but it can’t replace the legal work of matching your medical record to the incident facts.


