Many AI tools produce a number or range by taking generic inputs. But in real Selma cases, the outcome usually hinges on details that automation can’t reliably weigh—like whether symptoms were recorded promptly after the incident, how well follow-up care was documented, and whether the at-fault party’s conduct is clearly established.
Residents in the area often face a common mismatch:
- Symptoms may start mild and change over time (headache, dizziness, sleep disruption, concentration problems).
- Treatment may be delayed because of work schedules, transportation, or access to specialists.
- Evidence may be fragmented (ER records exist, but no follow-up notes connect the accident to ongoing cognitive issues).
Those gaps can shift how insurers frame severity and causation—meaning the “calculator” output may not match what a claim actually needs to succeed.


