AI tools usually work by taking inputs—diagnosis, symptom duration, treatment cost—and returning a range. That can be useful for organizing questions. However, in Mount Pleasant, claim outcomes still hinge on details that are difficult for generic models to capture, such as:
- How the incident occurred (head impact mechanics, witness observations, photos/video when available)
- Whether symptoms were documented early and consistently after the event
- Whether your medical timeline fits the story an insurer is willing to accept
- How your injury affected work and daily responsibilities (especially when treatment schedules and recovery vary)
If an AI estimate assumes a cleaner timeline than you actually have—or overlooks gaps common when people are trying to manage recovery while working—its “settlement range” may be misleading.


