AI tools typically produce a rough range based on common patterns—severity category, age, and broad assumptions about future care. Those outputs can be directionally helpful, but they often miss details that matter locally, such as:
- How quickly symptoms were documented after a crash on a high-speed roadway or after a slip/impact incident
- Whether the record clearly supports causation (that the spinal injury came from the event, not a pre-existing condition)
- The practical reality of long-term care planning in the Twin Cities region—therapy access, durable equipment needs, and caregiver availability
For catastrophic injuries, the strongest claims usually don’t come from a “calculator result.” They come from translating your medical reality into evidence insurers and adjusters can’t easily discount.


