Many AI tools generate a projected range by using common damage categories and simplified inputs. That can be useful for orientation, but in real Colorado spinal cord injury claims, insurers typically rely on evidence that’s more specific and more carefully documented than most calculators account for.
Common reasons AI estimates fall short:
- Causation proof is fact-specific. In Lone Tree, injuries may be linked to multi-car crashes on highway corridors, roadway hazards near busy intersections, or worksite incidents where multiple parties could argue blame.
- Neurological severity is not a single label. Two people can share the same general diagnosis while having very different functional limitations, complications, and medical trajectories.
- Future care requires documentation, not assumptions. Life-care planning is built from medical records, clinician recommendations, and functional assessments—elements an AI tool can’t truly “see.”
Instead of treating the output as a promise, use it as a checklist: what information would be needed to support the highest, most accurate damage picture?


