AI tools usually work by taking a few inputs—diagnosis, treatment, and symptom descriptions—and producing a general range. The problem is that TBIs are not just a label. In Ironton cases, the biggest mismatches often come from issues like:
- Gaps between the injury and consistent treatment. If symptoms flare after an accident but follow-up care is delayed (common when people are trying to manage work schedules), insurers may argue the injury wasn’t as severe as reported.
- Symptoms that overlap with everyday conditions. Headaches, dizziness, irritability, and sleep disruption can also be tied to migraines, stress, or other problems—so the record needs to connect the dots.
- Unclear timelines. In small-to-mid-size communities, people sometimes rely on memory. For TBI claims, memory is exactly what gets unreliable, so dates and documentation matter.
The takeaway: treat an AI result like a checklist of what to prove—not like a prediction of what you’ll receive.


