Many AI-style tools assume a clean timeline: impact → diagnosis → recovery that follows a predictable curve. Real cases are messier—especially in a city shaped by commuting traffic and frequent congestion.
For example, insurers often scrutinize:
- Delay between the crash and treatment (even if you were “shaken up” and symptoms evolved)
- Gaps in follow-up care (common when work schedules, rides, or appointments get complicated)
- Conflicting symptom descriptions (e.g., early notes mention dizziness, later notes focus on concentration problems)
An AI output may look precise, but it can’t verify whether your medical documentation matches the incident mechanics, nor can it anticipate how adjusters in Georgia build arguments for reduced value.


