AI tools typically generate a range by using general assumptions: injury severity, age, and broad categories of damages. That’s useful for orientation, but spinal cord injuries aren’t “one-size-fits-all.”
In Lincoln, the facts that often change outcomes include:
- Speed and impact context in traffic collisions (rear-end impacts, lane changes, and sudden braking on commute corridors)
- Delay between the crash and diagnosis (symptoms may be missed initially, especially if the patient is focused on pain control)
- Competing causation arguments (pre-existing conditions, prior injuries, or insurer claims that symptoms weren’t caused by the incident)
- Care needs after discharge (whether the patient can safely transfer, manage bowel/bladder needs, prevent skin breakdown, and access mobility equipment)
If an AI tool doesn’t have your complete medical record, imaging timeline, and functional limitations, it can produce numbers that look precise but aren’t grounded in your actual prognosis.


