Most AI tools work by taking inputs (injury severity, age, treatment, and care needs) and producing a broad range. The problem is that spinal cord injury outcomes aren’t “one-size-fits-all,” and the tool can’t see what Minnesota courts and insurance adjusters focus on.
In Ham Lake and nearby areas, common reasons an AI estimate can be misleading include:
- Unclear causation (especially when symptoms show up after the initial incident or there are intervening medical events)
- Incomplete documentation of neurological deficits—strength, sensation, spasticity, bowel/bladder function, skin risk, and mobility limitations
- Overlooked long-term needs tied to assistive devices and home/vehicle accessibility
- Assumed fault categories that don’t match what police reports, witness statements, and traffic reconstruction actually support
If your inputs are even slightly off—injury level, timeline to stabilization, or the level of daily assistance you require—the output can swing dramatically.


