AI-style tools often ask for details like diagnosis, treatment dates, and symptom reports. Then they generate a range that looks like a valuation.
In real Cortland cases, however, the outcome tends to hinge on details that aren’t captured well by generic models—like whether the injury’s timeline matches the medical record, whether follow-up care happened consistently, and how clearly the injury affected day-to-day functioning.
Common reasons AI estimates miss the mark include:
- Symptoms that are real but hard to quantify (brain fog, concentration problems, irritability)
- Gaps in treatment due to access, paperwork delays, or difficulty coping after the injury
- Causation disputes—insurance may claim the symptoms stem from something else
- Misunderstood “mild” injury narratives when a concussion evolves into persistent post-traumatic symptoms
So instead of treating AI output as a payout prediction, use it like a checklist: what evidence do I still need to document?


